The Memory Palace

Reading List

Like a todo-list but for knowledge. Ideally this page is auto-updated by the curating agent every morning as it reads the arXiv feeds…

26 Apr 2025

  • Improving ADME Prediction with Multitask Graph Neural Networks and Assessing Explainability in Lead Optimization
    Takuto Koyama, Shoma Ito, Shigeyuki Matsumoto, Ryosuke Kojima, Yuji Okamoto, Masataka Kuroda, Hitoshi Kawashima, Reiko Watanabe, Tomoki Yonezawa, Takaaki Sumiyoshi, Kazuyoshi Ikeda, Kenji Mizuguchi, Hiroaki Iwata, Yasushi Okuno
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-25
     The paper presents an AI model using multitask graph neural networks for predicting ten ADME parameters, addressing challenges in traditional methods. It utilizes fine-tuning and integrated gradients to assess feature contributions from a dataset of compounds. The model outperformed conventional methods for seven parameters and provided interpretable explanations aligned with chemical insights, enhancing drug development efficiency.

  • Bayesian Illumination: Inference and Quality-Diversity Accelerate Generative Molecular Models
    Jonas Verhellen
    Organic Chemistry on ChemRxiv
    2025-04-25
     The paper presents “Bayesian Illumination,” a new generative model that combines Bayesian optimization with quality-diversity archives to enhance molecule generation. It outperforms traditional genetic algorithms and deep learning methods in diversity and search efficiency. The study builds on previous work and utilizes bespoke kernels for small molecules.

  • Explaining Blood-Brain Barrier Permeability by Synergistic Effect on Molecular Substructures
    Hyun Woo Kim, Hyeok Jae Lee, Ikhyeong Jun
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-25
     The paper presents an interpretable machine learning algorithm to explain blood-brain barrier permeability (BBBP) through the synergistic effects of molecular substructures. By conducting relative importance analysis, the study identifies groups of substructures that influence BBBP positively or negatively, aiding in the design of drug candidates. The approach offers both interpretability and predictive capabilities for drug development.

  • Fractionation of Lignin with Aqueous Organic Solvents: A Step Closer to Sustainable Wood Biorefinery
    Henry Vider, Kait Kaarel Puss, Nikolai Treiberg, Siim Salmar, Mart Loog
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-25
     The paper discusses a method for purifying and fractionating hydrolysis lignin (HL) from birch using aqueous organic solvents, specifically water-ethanol (EtOH) and water-tetrahydrofuran (THF) mixtures. The highest lignin solubility (81 wt%) was achieved with a 70 wt% THF mixture, resulting in a 95 wt% purity with water-washing. The study also includes molecular dynamics simulations to explain solvation effects, improving HL valorisation opportunities.

  • Capturing Excited State Proton Transfer Dynamics with Reactive Machine Learning Potentials
    Umberto Raucci
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-25
     The paper explores excited state proton transfer using machine learning-driven molecular dynamics simulations. An active learning framework with enhanced sampling constructs a training set for excited state potentials. Applying this to 10-hydroxybenzo[h]quinoline, it finds barrierless transfer in ~50 fs and a 1 eV emission energy red shift, illustrating the method’s efficiency over ab initio approaches.

  • Metallaaromaticity Reimagined: Metallaaromatic Cobalt Macrocycles Through Metal-Ligand Coordination Chemistry
    Renana Gershoni-Poranne, Sakthi Raje, Katarzyna Młodzikowska-Pieńko, Amnon Stanger, Graham de Ruiter
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-25
     The paper discusses the synthesis of metallaaromatic cobalt macrocycles using a new carbene analog of the PyBOX ligand named CarBOX. The synthesis leads to complexes exhibiting strong diatropic currents, supported by 1H NMR shifts and DFT analysis. These findings provide a novel approach to metallaaromaticity in coordination chemistry, enhancing the design of electronic materials.

  • From short-sighted to far-sighted: A comparative study of recursive machine learning approaches for open quantum systems
    Arif Ullah
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-25
     The paper compares four physics-informed neural network architectures for modeling open quantum systems: SR-PINN, PSR-PINN, MR-PINN, and PMR-PINN. Using the spin-boson model and Fenna-Matthews-Olson complex, results show that multi-RDM models outperform single-RDM models in accuracy and stability for long-term predictions, while the inclusion of extra simulation parameters does not consistently enhance performance.

  • KGG: Knowledge-Guided Graph Self-Supervised Learning to Enhance Molecular Property Predictions
    Tuyen Truong, Tieu-Long Phan, Van-Thinh To, Phuoc-Chung Van-Nguyen, Gia-Bao Truong, Tuyet-Minh Phan, Rolf Fagerberg, Peter Stadler
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-25
     The paper introduces the Knowledge-Guided Graph (KGG) framework for molecular property prediction, addressing data scarcity and inadequate feature representation. It employs self-supervised learning with orbital-level features, utilizing approximately 250,000 molecules from the ZINC15 dataset. Results show significant improvements over state-of-the-art methods, validated through extensive evaluations and complementary analyses.

  • Physics-Informed Machine Learning Enables Rapid Macroscopic pKa Prediction
    Corin Wagen
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-25
     The paper introduces Starling, a physics-informed neural network for predicting macroscopic pKa values using the Uni-pKa architecture. It resolves protonation and tautomeric microstates, outperforming commercial tools on benchmark datasets. Starling supports accurate predictions of isoelectric points and drug properties, offering rapid microstate ensemble generation while maintaining thermodynamic consistency.

  • Quantum QSAR for drug discovery
    Mariano Caruso, Alejandro Giraldo, Daniel Ruiz, Guido Bellomo
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-25
     The paper proposes using Quantum Support Vector Machines (QSVMs) to enhance Quantitative Structure-Activity Relationship (QSAR) modeling in drug discovery. This method addresses classical QSAR limitations in high-dimensional data and complex molecular interactions by utilizing quantum data encoding and kernel functions for improved predictive accuracy and efficiency.

25 Apr 2025

  • Visualizing biomolecules within living microglia in complex environments using a clickable small fluorogenic compound
    Wonju Kim, Beomsue Kim, Young-Tae Chang, Yeongran Hwang, Xiao Liu, Minkyo Jung, Ji-Young Mun, Srikanta Sahu
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-24
     The paper presents the development of the fluorescent probe CDr20-CO1 for visualizing biomolecules in live microglia. It reveals that microglia in mixed cultures showed reduced responses to neuroinflammatory stimuli, highlighting the importance of their environment. The probe also functions in live mouse embryos, enhancing the potential for in vivo studies of microglial dynamics in neurodegenerative diseases.

  • Characterizing RNA Tetramer Conformational Landscape Using Explainable Machine Learning
    Dhiman Ray, Sompriya Chatterjee
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-24
     The paper presents a simulation framework combining explainable AI with the OPES algorithm to sample RNA tetramer conformational landscapes. It captures states like stacked and intercalated structures efficiently with reduced computational effort. The model identifies key torsion angles affecting transitions, facilitating improvements in nucleic acid force fields. Results highlight the method’s effectiveness in uncovering metastable states in RNA simulations.

  • Advancing Density Functional Tight-Binding method for Large Organic Molecules through Equivariant Neural Networks
    Leonardo Rafael Medrano Sandonas, Mirela Puleva, Ricardo Parra Payano, Martin Stöhr, Gianaurelio Cuniberti, Alexandre Tkatchenko
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-24
     The paper introduces the EquiDTB framework, utilizing equivariant neural networks to enhance the density functional tight-binding (DFTB) method for larger organic molecules. It replaces standard pairwise potentials, improving accuracy for non-covalent systems and expanding applications to larger molecules. The method shows superior performance in calculating atomic forces and interaction energies compared to standard TB methods, demonstrating DFT-PBE0 level accuracy efficiently.

  • Deep Learning Methods for 2D Material Electronic Structures
    Artem Mishchenko, Anupam Bhattacharya, Xiangwen Wang, Henry Pentz, Yihao Wei, Qian Yang
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-24
     The paper reviews the application of deep learning techniques in predicting electronic structures of 2D materials, addressing challenges related to these materials. It highlights methods like physics-aware models and generative AI, which enhance predictions of electronic properties. Case studies demonstrate accelerated discoveries in quantum phenomena, with a call for improved data standardization and integrated frameworks.

  • Enhancing deep chemical reaction prediction with advanced chirality and fragment representation
    FABRIZIO MASTROLORITO, Fulvio Ciriaco, Orazio Nicolotti, Francesca Grisoni
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-24
     The paper introduces fragSMILES, a fragment-based chemical representation that enhances deep learning for reaction prediction. It outperformed established methods (SMILES, SELFIES, SAFE) in both forward and retro-synthesis, particularly in recognizing stereochemical information. The findings show that incorporating chirality and fragment representation significantly improves synthesis planning capabilities.

  • Data-driven discovery of chemical reaction mechanisms from limited concentration profiles
    Shun Hayashi
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-24
     The paper presents a method for discovering chemical reaction mechanisms using sparse identification with limited concentration profiles. This approach, which avoids overfitting, was validated in the autocatalytic reduction of manganese oxide ions, revealing 11 elementary steps involving 8 species. The method’s capability for automated discovery without heuristic models suggests broader applications in scientific research with limited data.

  • Condensed phase properties and transferable neural network potentials
    Stefan Boresch, Anna Katharina Picha, Marcus Wieder
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-24
     The paper evaluates the performance of two transferable neural network potentials (ANI-2x, MACE-OFF23-(S/M)) in predicting condensed phase properties such as density and heat capacity for several pure liquids (e.g., water, benzene). Results indicate current models have weaknesses affecting their accuracy in this context, highlighting the need for incorporating condensed phase property validation in NNP training.

  • QC-Augmented GNNs for Few-Shot Prediction of Amorphous Polymer Properties via Multi-Scale Microstructures
    Yiwen Zhang, Zihao YE, Dejun HU, Shutao QI, Zuobang SUN, Junfeng YANG, Yan MA, Yi ZHANG, Junliang ZHANG, Zhiming LI
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-24
     The paper presents Locluster, a novel model that utilizes “local clusters” derived from quantum chemistry to predict properties of amorphous polymers. It integrates these descriptors with graph convolutional networks, requiring only 2-5 descriptors and about 20 training samples. Results show Locluster achieves comparable accuracy to larger datasets, addressing data scarcity in polymer informatics for properties like density and glass transition temperature.

  • Bioinspired Design Rules for Flipping across the Lipid Bilayer from Systematic Simulations of Membrane Protein Segments
    Reid Van Lehn, ByungUk Park
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-24
     The paper investigates the flipping behavior of the membrane protein EmrE using molecular dynamics simulations. Key findings show that a charged glutamate residue significantly lowers the energetic barrier for flipping. The study establishes design principles for synthetic materials, suggesting that marginally hydrophobic helices and modulating protonation states can enhance flipping, relevant for drug delivery and protein design.

  • Towards a Theoretical Understanding of Excitonic Properties of Pthalocyanine Thin Films I Low-Temperature Exciton Absorption Spectra
    Yihan Shao, Sanghita Sengupta, Zheng Pei, Chance Lander, Yu Zhang, Madalina Furis, Carly Wickizer, Yu Homma, Pengfei Huo, Hadi Afashari, Sergei Tretiak, Lloyd Bumm
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-24
     The paper investigates the excitonic properties of phthalocyanine thin films, modeling low-temperature absorption spectra using the Frenkel Hamiltonian. It highlights the effects of dimensionality and coupling distances on exciton behavior, particularly for octabutoxy phthalocyanine. Results indicate that accurate modeling requires considering 2D or 3D configurations and various exciton couplings, warning against overestimation from dipole-dipole approximations.

  • Enabling in-silico Hit Discovery Workflows Targeting RNA with Small Molecules
    Yuqi Zhang, Nikita Chopra, Ara Abramyan, Zeineb Si Chaib, Wei Chen, Yeyue Xiong, David Rinaldo, Steven Jerome, Anna Bochicchio
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-24
     The paper discusses advancements in computational tools for RNA-small molecule interaction modeling within the Schrödinger Suite. Enhanced SiteMap improved binding site prediction accuracy to 65.3% on the HARIBOSS dataset, while Glide achieved 73.7% pose prediction accuracy. Additionally, absolute binding free energy perturbation showed a good correlation with experimental affinities (RMSE of 1.10 kcal/mol). Further refinements are needed for targeting RNA.

  • Electron Density Transport During Chemical Reactions
    Jackson Elowitt, Aurora Clark, Nathan May, Yihui Wei, Biswajit Sadhu, Enrique Alvarado, Bala Krishnamoorthy
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-24
     The paper investigates electron density transport during reactions using Optimal Transport (OT) and Multiresolution Dynamic Mode Decomposition (MrDMD). It applies these methods to the Bergman cyclization reaction, demonstrating OT’s chemical insight and MrDMD’s effective reconstruction of density evolution. The study highlights datasets and processes useful for high-throughput analysis in complex environments.

24 Apr 2025

  • Single-Pot Mechanochemically-Enabled Fluorine Atom Closed- Loop Economy Using PFASs as Fluorinating Agents
    Georgina, Kirby; Hao, Long; Lutz, Ackermann
    Organic Chemistry on ChemRxiv
    2025-04-23
     The paper presents a sustainable, single-pot mechanochemical method for defluorinating PFASs, allowing efficient fluorine transfer to organic molecules. The approach requires minimal solvent filtration, making it user-friendly and scalable. It can also degrade everyday fluoroplastics, addressing environmental concerns associated with ‘forever chemicals.’

  • Small Molecule Activation by Metallylenes and Their Follow-up Reactions
    Trevor, Hamlin; Eveline, Tiekink; Matthijs , Kragtwijk
    Organic Chemistry on ChemRxiv
    2025-04-23
     The paper reviews the role of metallylenes, heavy analogs of carbenes, in activating small molecules for sustainable chemistry. It discusses their mechanisms, the impact of ligand design, and recent computational and experimental advances. It also covers follow-up reactions like hydrogenation and hydroboration, highlighting metallylenes’ catalytic potential and versatility in chemical transformations.

  • Compounds reducing human sperm motility as potential non hormonal contraceptives identified using a high-throughput phenotypic screening platform
    Anthony, Richardson; Franz, Gruber; David, Day; Irene, Georgiou; Zoe, Johnston; Sarah, Martins da Silva; Rachel, Myles; Neil, Norcross; Halimatu, Joji; Darren, Edwards; Kevin, Read; Jason, Swedlow; Caroline, Wilson; Christopher, Barratt; Ian, Gilbert
    Organic Chemistry on ChemRxiv
    2025-04-23
     The paper explores the identification of potential non-hormonal contraceptive compounds that reduce human sperm motility through a high-throughput phenotypic screening of 88,773 compounds from nine libraries. Using an automated robotic platform, researchers identified nine chemical series with selective sperm motility reduction and minimal cytotoxicity to HepG2 cells, indicating their potential for further contraceptive research.

  • Generality-driven optimization of enantio- and regioselective catalysis by high-throughput experimentation and machine learning
    Do Hyun, Ryu; Hyunwoo, Kim; Joonsuk, Huh; Terim, Seo; Donghun, Kim; Shinwon, Ham; You Kyoung, Chung; Inho, Jeong
    Organic Chemistry on ChemRxiv
    2025-04-23
     This paper presents a high-throughput experimentation and machine learning approach for optimizing enantio- and regioselective catalysis of unsymmetrical 1,2-dicarbonyl compounds. The method combines quantitative 1H and 19F NMR to screen 31 chiral oxazaborolidinium ion variants, yielding chiral alpha-silyloxy ketones with >99% yield and selectivity. The ARMS system enhances data efficiency for catalyst screening and enables new substrate exploration.

  • AI-Driven Antimicrobial Peptide Discovery: Mining and Generation
    Paulina, Szymczak; Wojciech, Zarzecki; Jiejing, Wang; Yiqian, Duan; Jun, Wang; Luis Pedro , Coelho; Cesar , de la Fuente-Nunez; Ewa, Szczurek
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-23
     The paper discusses the role of AI in discovering antimicrobial peptides (AMPs) to combat antimicrobial resistance (AMR). It highlights strategies of AMP mining and generation for identifying and creating novel peptides with optimal properties. The integration of diverse data sources and advanced algorithms has led to successful identification and validation of promising candidates, showcasing AI’s transformative potential in drug discovery.

  • One To Rule Them All: A Universal Interatomic Potential Learning Across Quantum Chemical Levels
    Pavlo O., Dral; Yuxinxin, Chen
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-23
     The paper presents OMNI-P1, a universal interatomic potential that learns across multiple quantum chemical levels, overcoming challenges associated with heterogeneous datasets. OMNI-P1 achieves comparable performance to GFN2-xTB and DFT methods while being significantly faster. Additionally, the delta-learning model OMNI-P1d offers superior accuracy by providing corrections to lower-level predictions.

23 Apr 2025

  • Assay-Based Machine Learning: Rethinking Evaluation in Drug Discovery
    Michael Backenküehler, Joschka Groß, Andrea Volkamer
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-22
     The paper discusses a novel approach to machine learning in drug discovery, emphasizing evaluation aligned with real experimental contexts. It leverages data from ChEMBL, proposing a method that splits data by entire assays and focuses on intra-assay ranking. Results show improved performance of ranking models compared to traditional methods, especially with minimally curated data.

  • Activity-Based Chemoproteomic Profiling Reveals the Active Kinome of Leishmania
    Exequiel O. J. Porta, Karunakaran Kalesh, Patrick G. Steel
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-22
     The paper explores the active kinome of Leishmania mexicana using activity-based protein profiling (ABPP) with custom ATP-directed probes. The study identifies 48 active kinases across major families, including 9 essential protein kinases with no human orthologs. The findings suggest these kinases as promising drug targets, paving the way for next-generation therapies against leishmaniasis.

  • CACHE: Utilizing Ultra-Large Library Screening in Rosetta to Identify Novel Binders of the WD-Repeat Domain of Leucine-Rich Repeat Kinase 2
    Fabian Liessmann, Paul Eisenhuth, Alexander Fürll, Oanh Vu, Rocco Moretti, Jens Meiler
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-22
     The study presents a novel pipeline for identifying ligands targeting LRRK2’s WDR40 domain, crucial for Parkinson’s research, using an ultra-large library screening (ULLS) with RosettaEvolutionaryLigand (REvoLd). The method yielded five potential binders, emphasizing the approach’s efficiency despite limitations noted in the scoring function. Key datasets included the Enamine REAL space library.

  • Chemistry of Scorpion Venom and its Medicinal Potential
    Shreerang Joshi, Khetal Surana, Shashwat Singh, Aditya Lade, Nitin Arote
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-22
     The paper reviews scorpion venom’s chemical composition, categorizing toxins into groups such as α-toxins and KTxs, and discusses their pharmacological effects and potential therapeutic applications in cancer treatment and antimicrobial therapies. It highlights challenges like toxicity and non-selectivity, emphasizing the need for further research to maximize its medicinal potential.

  • Investigation of Effective Molecular Dynamics-derived Properties on Drug Solubility via Machine Learning
    Zeinab Sodaei, Saeid Ekrami, Seyed Majid Hashemianzadeh
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-22
     The paper explores the use of machine learning to analyze the impact of ten molecular dynamics (MD)-derived properties and LogP on drug solubility. Utilizing a dataset of 199 drugs, the study identifies seven key properties influencing solubility. The Gradient Boosting algorithm achieved an R² of 0.87 and RMSE of 0.537, showcasing the integration of MD simulations and machine learning in enhancing solubility predictions.

  • COARSE-GRAINED MOLECULAR DYNAMICS SIMULATIONS OF SOFT MATTER RELEVANT TO THE PHARMACEUTICAL INDUSTRY
    Meenakshi Dutt, Rishabh K. Singh, Yiwei Shao, Het Patel, Mason Hooten
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-22
     The paper discusses coarse-grained molecular dynamics simulations of soft materials relevant to the pharmaceutical industry, highlighting their role in formulation and drug delivery. It emphasizes using CG models alongside MD simulations to explore various soft matter applications and provides examples of simulations for different molecular chemistries.

22 Apr 2025

  • AI0Driven Integrative Design of ADAMTS Partial Agonists within a Multi0Hallmark Therapeutic Framework for Neurodegenerative Disorders
    David , Ferguson, RSci MRSB MRSC
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-21
     The paper presents an AI-driven framework for designing ADAMTS partial agonists targeting neurodegenerative disorders. It utilizes large language models, AlphaFold2 structures, and MolGPT for retrosynthesis and candidate optimization. The approach evaluates compounds based on in silico metrics like binding energy and solvation profile, aiming to restore ECM integrity and mitigate neuroinflammation in diseases such as Alzheimer0s and Parkinson0s.
  • Choline-Geranate (CAGE) Ionic Liquids Potentiate The Anticancer Activity Of Platinum-Based Drugs
    Luca, Salassa; Marta, Costa Verdugo; Giulia , Sierri; Laura , Hernandez-Fernandez; Elena , Formoso; Jos0 I. , Miranda; Francesco Saverio , Sica; Alba , Gonz0lez; Elixabete, Rezabal; Francesca , Re
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-21
     The paper investigates the effect of choline-geranate ionic liquids (CAGE ILs) on platinum-based anticancer drugs. Hydro-solubility increased, with notable stability in one Pt(IV) complex. In U87 glioblastoma cells, CAGE ILs enhanced cytotoxicity, particularly for one complex, which significantly decreased cell viability. Methods included NMR analysis and cell viability assays.
  • Finding the Dark Matter: Large Language Model-based Enzyme Kinetic Data Extractor and Its Validation
    Xinchun, Ran; Galen, Wei; Runeem, Al-Abssi; Zhongyue, Yang
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-21
     The paper presents EnzyExtract, a large language model-based pipeline that automates the extraction of enzyme kinetic data from 137,892 publications, resulting in 218,810 structured entries. This effort significantly expands enzymology datasets, yielding 94,576 unique entries missing from BRENDA. The curated data enhances predictive modeling, as evidenced by improved performance of kcat predictors.

19 Apr 2025

  • Finding Drug Candidate Hits With a Hundred Samples: Ultra-low Data Screening With Active Learning
    Jan H., Jensen; Jacob M., Nielsen; Maria H., Rasmussen; Casper, Steinmann; Nicolai, Ree; Michael , Gajhede; Jan, Stenvang
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-18
     The paper explores active learning (AL) for drug discovery using minimal data, specifically 110 affinity evaluations from the DTP and DDS-10 datasets. The study identifies the optimal combination of Continuous and Data-Driven Descriptors with a Multi-Layer Perceptron and PADRE data augmentation, achieving a 97% success rate for top hits. Enriching datasets with known hits enhances results, showcasing AL’s potential in resource-limited settings.

  • Application of vision transformers to protein-ligand affinity prediction
    Pawel, Siedlecki; Jakub, Poziemski
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-18
     The paper explores using Vision Transformers for predicting protein-ligand binding affinity from 3D structural data. Evaluating on two benchmark datasets, the approach shows competitive results, surpassing state-of-the-art models in some instances. The study highlights the importance of hyperparameter tuning and data augmentation, demonstrating the model’s ability to capture key interaction features and suggesting its broader applicability.

  • Sustainable Optimization of Extraction Techniques for Bioactive Compounds in Drug Discovery: Principles, Innovations, and Case Studies
    Marvellous, Eyube; Anita, Ohwoevwo; Susan, Babayanju; Frances, Okechukwu; Eden, Osadolor; Favour, Boco; Marvellous , Alimikhena; Etinosa, Agbonghae
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-18
     The paper discusses sustainable optimization of extraction techniques for bioactive compounds in drug discovery. It evaluates methods like Supercritical Fluid Extraction and Microwave-Assisted Extraction through case studies, including curcuminoids and phenolics extraction, showcasing improved purity and yield with AI integration. It highlights challenges in scaling and recommends best practices for enhancing the efficiency and sustainability of drug discovery processes.

  • Keep looking at the negative side: improved detection of drug-induced liver injury with non-hepatotoxicant data oversampling
    Olivier JM, BÉQUIGNON; Steven, Wink; Steven W., Hiemstra; J. E., Fokkelman-Klip; Wouter, den Hollander; Giulia, Callegaro; Bob, van de Water; Gerard J. P., van Westen
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-18
     The paper presents an improved method for detecting drug-induced liver injury (DILI) by using a custom oversampling strategy to address class imbalance in datasets. It integrates stress pathway activations with molecular descriptors and bioactivity data, enhancing predictive accuracy. Results indicate improved specificity, yet challenges remain in identifying key biomarkers and optimizing data acquisition for better predictive models.

  • Optimizing Bio-Imaging with Computationally Designed Polymer Nanoparticles
    Anupom, Roy; Connard Giresse, Tetssassi Feugmo
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-18
     The paper examines poly(p-phenylene ethyny-lene) nanoparticles (PPE-NPs) for bio-imaging using molecular dynamics simulations and time-dependent density functional theory (TD-DFT) to analyze their structural and optical properties. Findings include strong fluorescence and efficient electronic behavior, with the M05 functional predicting absorption wavelengths accurately. This research enhances the potential of PPE-NPs as fluorescent probes for biomedical applications.

  • Accelerating the Structure Exploration of Diverse Bi–Pt Nanoclusters via Physics-Informed Machine Learning Potential and Particle Swarm Optimization
    Carine, Clavaguera; Nguyen-Thi, Van-Oanh; Raphaël, Vangheluwe; Dominik, Domin; Mihai-Cosmin, Marinica; Minh-Tue, Truong; Cong Huy, Pham
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-18
     The paper presents a physics-informed machine learning approach combined with particle swarm optimization to classify 34 Bi-Pt nanoclusters, achieving 87% accuracy. Utilizing DFT data and techniques like PCA and K-means clustering, the study explores structural motifs, cohesive energy trends, and vibrational properties, contributing to an automated method for analyzing bimetallic nanoclusters’ stability and functionality.

  • Enabling in-silico Hit Discovery Workflows Targeting RNA with Small Molecules
    Yuqi, Zhang; Nikita, Chopra; Ara, Abramyan; Zeineb, Si Chaib; Wei, Chen; Yeyue, Xiong; David, Rinaldo; Steven, Jerome; Anna, Bochicchio
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-18
     The study enhances in-silico hit discovery for RNA-targeted drug design using the Schrödinger Suite. It improves RNA binding site prediction accuracy to 65.3% using an enhanced SiteMap on the HARIBOSS dataset and achieves a 73.7% accuracy in pose prediction with revamped Glide. The absolute binding free energy perturbation method shows a 1.10 kcal/mol RMSE for 5 RNA-ligand systems, highlighting the potential for RNA-target drug discovery.

  • Strong field quantum control of bimolecular reactions
    Jyotirmoy, Ray; Tamar , Seideman
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-18
     The paper presents a quantum control strategy for polyatomic bimolecular reactions using moderate-intensity laser fields, addressing challenges from complex dynamics and thermal averaging. By manipulating spatially nonuniform Stark shifts via a polarized field, the researchers enhance reaction rates in the CH + N2 system, supported by quantum mechanical theory and ab-initio calculations. Results suggest new control opportunities in bimolecular reactions.

18 Apr 2025

  • WatCon: A Python Tool for Analysis of Conserved Water Networks Across Protein Families
    Shina Caroline Lynn, Kamerlin; Alfie-Louise R., Brownless; Travis, Harrison-Rawn
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-17
     The paper presents WatCon, an open-source Python tool for analyzing conserved water networks across protein families, utilizing dynamic and static structural data. It allows classification and characterization of water networks. Demonstrated through applications on proteins like PTP1B and TPI, WatCon aids in understanding biochemical systems and predicting water hotspots relevant to protein engineering. Available at GitHub.
  • Discovery of YTHDF2 ligands by fragment-based design
    Annalisa, Invernizzi; Francesco, Nai; Rajiv Kumar, Bedi; Pablo Andrés, Vargas-Rosales; Yaozong, Li; Elena, Bochenkova; Marcin, Herok; František, Zálešák; Amedeo, Caflisch
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-17
     The paper reports the discovery of six YTHDF2 ligands via fragment-based high-throughput docking, achieving a hit rate of 13%. A total of 28 analogues and 32 derivatives were synthesized, resulting in low-micromolar affinity compounds, with compound 23 showing IC50 values of 2 µM and 10 µM. Six crystal structures of YTHDF2-ligand complexes were also revealed.
  • Programmable Site-Selectivity: pH-Modulated Triazine-Thiol Exchange for Site- and Chemoselective Cysteine Labelling
    Kevin, Neumann; Katerina , Gavriel; Daniel, Deißenbeck; Thomas J., Rutjes; Daniëlle W. T. , Geers; Jan , Meisner
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-17
     The paper presents a programmable strategy for site-selective cysteine modifications using pH-modulated triazine-thiol exchange. It achieves regulation of cysteine labelling at different pH levels, enabling modification of internal or N-terminal cysteines. The research employs molecular dynamics simulations and density functional theory to explain the site-selectivity mechanism, enhancing precision in peptide engineering.
  • Indirect Ubiquitination: Noncovalent Ubiquitin Tethering Independent of Endogenous Ubiquitination Machinery for Targeted Protein Degradation
    Akimitsu, Okamoto; Takafumi, Furuhata; Kazuki, Yoshida; Ryoka, Fujita; Jotaro, Miyamoto; Chiharu, Moriyama; Tokiha, Masuda-Ozawa; Hikaru, Tsuchiya; Yasushi, Saeki
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-17
     The paper discusses a novel method of indirect ubiquitination using heterobifunctional degrader molecules that enable non-covalent ubiquitin tethering, circumventing the need for endogenous ubiquitination machinery. This approach allows for the targeted degradation of proteins, including recombinant and endogenous types, and offers a solution to issues related to drug resistance and E3 ligase activity impairment.
  • DNA-Encoded Chemical Library Screening with Target Titration Analysis: DELTA
    John C., Faver; Flora, Sundersingh; Lauren A., Viarengo-Baker; Ying-Chu, Chen; Katelyn, Billings; Patrick, Riley; Ching-Hsuan, Tsai; Christopher S., Kollmann
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-17
     The paper presents DELTA, a DNA-Encoded Library screening method that incorporates target titration analysis to improve binding affinity predictions for Bruton’s tyrosine kinase. It uses a split-sample strategy and a probabilistic model to enhance data quality, yielding better ranking of compounds compared to traditional metrics and successfully identifying potent members otherwise missed.
  • Nanoscale direct-to-biology optimization of Cdk2 inhibitors
    James L., Douthwaite; Damian J., Houde; Eneida H., Pardo; Mark S., Moran; Jason, Baird; Sophia R., Meyer; Qiyuan, Zhao; Jay F., Larrow; Babak, Mahjour; Yu-Pu, Juang; Calvin K., Han; Brian, Kelley; David R., Dunstan; Katelyn J., Billings; Mary M., Mader; Alexander M., Taylor; Jonathan Z., Sexton; Alessandro A., Boezio; Tim, Cernak
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-17
     The paper presents a miniaturized workflow for optimizing Cdk2/CycE inhibitors using direct-to-biology (D2B) methods with ultrahigh-throughput experimentation (ultraHTE) in 1,536 well plates. Assays included functional biochemical assessments and phenotypic cell painting, revealing effective lead inhibitors that induced cell cycle arrest at G0. This approach streamlines optimization by integrating multiple steps in one experiment.
  • Understanding the Mechanism of CO2 to CH4 Conversion and Hydrogen Evolution Reaction on Mg Nanoparticles in Water under ambient conditions: A DFT Perspective
    Amit, Mondal; Munia, Sultana; Ankan, Paul
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-17
     The paper explores CO2 conversion to methane and hydrogen using magnesium nanoparticles at ambient conditions through Density Functional Theory (DFT). It identifies reaction pathways involving Mg clusters (Mg22 and Mg56), revealing key processes such as the formation of Mg-H and Mg-OH bonds. Notable activation barriers for methanol formation (16.9 kcal/mol) and CH4 release are discussed.
  • Introducing PolySea: An LLM-Based Polymer Smart Evolution Agent
    Haoke, Qiu; Zhao-Yan, Sun; Jichun, Zhao; Enzhe, Jing; Weilong, Hu; Yurun, Lv; Xuefeng, Li
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-17
     The paper introduces PolySea, a domain-specific LLM for polymer informatics, trained on a curated dataset combining high-fidelity polymer properties and expert knowledge. It achieves an R² score of 0.97 in regression and 79% accuracy in thermal stability classification. Comparative results show PolySea outperforms general-purpose LLMs, generating novel polymer structures validated for real-world synthesis.
  • Large-Scale Ab Initio Molecular Dynamics for Assessing Stabilities of Near-Surface NV Centers
    Bryan, Wong; Kamal, Sharkas; Gabriel, Phun; Sohag, Biswas
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-17
     The paper investigates the stability of near-surface nitrogen vacancy (NV) centers using ab initio molecular dynamics. The study finds that NV centers near the 111 surface are more stable than deeper defects and can withstand temperatures up to 1000°C. Techniques like Crystal Orbital Hamilton Population analyses are used to analyze electronic properties, essential for developing quantum sensors.

17 Apr 2025

  • Allostery Without Conformational Change: A Native Mass Spectrometry Perspective
    David, Russell; He Mirabel, Sun; Kacie, Evans; Morgan, Powers; Zhenyu, Xi; Carter, Lantz; Arthur, Laganowsky; Hays, Rye
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-16
     The paper investigates the effects of variable temperature (vT)-nESI-MS and ion mobility spectrometry (IMS) on GroEL single ring mutant (SR1) ligand binding, using datasets with varying temperatures (2-100 °C) and three ESI buffers. Results showed differences in ADP binding cooperativity and thermodynamics, emphasizing the impact of buffer compositions on protein dynamics and conformations.

  • Keep looking at the negative side: improved detection of drug-induced liver injury with non-hepatotoxicant data oversampling
    Olivier JM, BÉQUIGNON; Steven, Wink; Steven W., Hiemstra; J. E., Fokkelman-Klip; Wouter, den Hollander; Giulia, Callegaro; Bob, van de Water; Gerard J. P., van Westen
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-16
     The paper presents a novel oversampling strategy to enhance drug-induced liver injury (DILI) detection by integrating stress pathway activations and molecular data to address class imbalance. Results showed improved predictive accuracy without overfitting. However, challenges remain in identifying key biological markers and optimizing data acquisition for more reliable DILI models.

  • A Multi-Task Learning Approach for Data Imputation of Compound Bioactivity Values for the SLC Transporter Superfamily
    Tarik, Cerimagic; Sergey, Sosnin; Gerhard, Ecker
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-16
     The paper presents a multi-task deep neural network (MTDNN) to impute missing compound-bioactivity values for the SLC transporter superfamily. Using a dataset with a 2.53% density, it generated 480,133 predictions for 9,122 compounds across 54 targets with an R² of 0.74, outperforming single-task learning approaches and demonstrating effective data imputation despite data limitations.

  • Surface Hopping Molecular Dynamics Simulations for Photophysics and Photoreactions Involving Pyrene and CH3Cl
    Evgenii, Titov; Elham, Mazarei; Peter, Saalfrank
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-16
     The paper investigates the photophysics and photoreaction dynamics of pyrene (C16H10) and methyl chloride (CH3Cl) using non-adiabatic surface hopping molecular dynamics simulations. It employs semiempirical (AM1) methods and (time-dependent) density functional theory for structural optimizations and excited-state analysis, revealing insights into excited-state lifetimes and photochemical behavior of these compounds.

  • Computationally-Guided Development of Sulfide Solid Electrolyte Powder Coatings for Enhanced Stability and Performance of Solid-State Batteries
    Justin, Connell; Aditya, Sundar; Taewoo, Kim; Francisco, Lagunas; Anil, Mane; Udochukwu, Eze; Sanja, Tepavcevic; Rajesh, Pathak; Jeffrey, Elam; Zachary, Hood; Peter, Zapol
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-16
     The paper presents a density functional theory-based protocol for identifying new oxide coatings to enhance sulfide solid-state electrolytes (SSEs) like Li6PS5Cl. It demonstrates that MgO coatings significantly improve stability and performance metrics. Key findings emphasize the importance of ionic and electronic conductivity at interfaces, paving the way for advancements in solid-state battery technology.

  • Potential energy surfaces: Δ-machine learning from analytical functional forms
    Cipriano, Rangel; Joaquin, Espinosa-Garcia
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-16
     The paper presents delta-machine learning (Δ-ML) as an efficient method for creating high-level potential energy surfaces (PES) from low-level data. Utilizing the analytical PES-2008 and information from the PIP-NN surface, Δ-ML is applied to the H + CH4 reaction. Kinetic and dynamic studies affirm Δ-ML’s accuracy, showing it can effectively describe complex polyatomic systems.

  • OMNI-P2x: A Universal Neural Network Potential for Excited-State Simulations
    Pavlo O., Dral; Mikołaj, Martyka; Xin-Yu, Tong; Joanna, Jankowska
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-16
     OMNI-P2x is a universal neural network potential developed for efficient excited-state simulations, achieving near time-dependent density functional theory accuracy at a lower cost. It outperforms traditional semiempirical quantum methods and is demonstrated in UV/Vis spectroscopy, real-time photodynamics, and designing visible-light-absorbing azobenzene. This method enhances high-throughput investigations of photo-active molecular systems in scientific applications.

  • Theoretical kinetics study of the OH + CH3SH reaction based on an analytical full-dimensional potential energy surface
    Cipriano, Rangel; Joaquín, Espinosa-Garcia
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-16
     The paper studies the OH + CH3SH reaction using a newly developed potential energy surface (PES-2024) and three kinetic theories: VTST, QCT, and RPMD. It analyzes thermal rate constants from 200-1000 K, finding a V-shaped temperature dependence with discrepancies between methods. Kinetic isotope effects exhibited both normal and inverse behaviors depending on the reaction path.

  • Reaction Discovery Using Bayesian Optimization: Lithium Salt Directed Stereoselective Glycosylations
    Natasha Videcrantz, Faurschou; Christian Marcus, Pedersen
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-16
     The paper explores using Bayesian optimization for discovering new stereoselective glycosylation methods, treating the glycosylation reaction as a black box. By introducing lithium salts, novel methodologies were identified. The study demonstrates the utility of partial dependence plots for interpreting results, enhancing understanding of complex reaction spaces.

  • Effect of Thermal Disorder on the Electronic Structure and the Charge Mobility of Acenes
    Alessandro, Landi; Francesco, Ambrosio; Anna, Leo; daniele, Padula; Giacomo, Prampolini; Andrea, Peluso
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-16
     The paper investigates the impact of thermal disorder on the electronic structure and charge mobility of naphthalene and pentacene using molecular dynamics and ab initio calculations. Results indicate that thermal disorder reduces charge mobility due to polaronic energy level shifts and site energy fluctuations, aligning well with experimental data. Kinetic Monte Carlo simulations were utilized for evaluating charge mobilities.

  • Development of Parallel On-the-Fly Crystal Algorithm for Reaction Discovery in Large and Complex Molecular Systems
    Ruibin, Liang; Ankit, Pandey; Gustavo J., Costa; Alam, Mushfiq; Bill, Poirier
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-16
     The paper presents a parallel on-the-fly Crystal algorithm for efficient exploration of potential energy surfaces in complex molecular systems, applied to bilirubin and donor-acceptor Stenhouse adducts. An automated workflow facilitated the discovery of new minima and reaction pathways in vacuum and aqueous solutions, demonstrating the algorithm’s effectiveness in uncovering kinetically accessible products and enhancing understanding of chemical reactivities in varied environments.

  • Water-mediated interactions between glycans are weakly repulsive and unexpectedly long-ranged
    Sucheol, Shin; Mauro, Mugnai; Dave, Thirumalai
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-16
     The paper reveals through all-atom molecular dynamics simulations that water-mediated interactions between N-glycans are weakly repulsive and long-ranged, decaying logarithmically with increased separation. This finding links glycan interactions to star polymer behavior and highlights the role of electrostatic interactions and hydrogen bonds in mediating these effects.

16 Apr 2025

  • Twenty-Five years of High-throughput Screening of Biological Samples with Mass Spectrometry: Current Platforms and Emerging Methods
    Perdita Barran, Michael Morris, Rachel Smith, Catherine Brookes
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-15
     The paper reviews 25 years of high-throughput screening (HTS) using mass spectrometry (MS), highlighting advances in automation and ionisation techniques that improve efficiency and sustainability. It discusses the challenges faced in MS adoption compared to optical methods, and reflects on lessons learned from the COVID-19 pandemic.

  • A Foundation Model for Accurate Atomistic Simulations in Drug Design
    Jean-Philip Piquemal, Thomas Plé, Louis Lagardère, Olivier Adjoua, Evgeny Posenitskiy, Corentin Villot, Anouar Benali
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-15
     The paper discusses FeNNix-Bio1, a foundation model trained exclusively on synthetic quantum chemistry data for atomistic simulations in drug design. It outperforms traditional methods, addressing challenges like fast inference and model transferability, particularly for charged species. The model successfully simulates water properties, protein dynamics, and binding free energies, enhancing its applicability in drug design.

  • Accurate Electrostatics for Biomolecular Systems through Machine Learning
    David van der Spoel, A. Najla Hosseini, Kristian Kriz
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-15
     The paper discusses a machine learning approach using the Alexandria Chemistry Toolkit (ACT) to improve electrostatic interaction modeling in biomolecular systems. It critiques traditional charge fitting methods and presents novel models for charged amino-acid side chain analogs, leading to enhanced prediction of electrostatic and induction energies, surpassing existing models in accuracy.

  • Comparative Analysis of Molecular Embeddings for Efficient Compound Similarity Search Using Vector Databases
    Krzysztof Baczyński, Anna Szymańska, Marcin Król
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-15
     The paper compares various molecular embedding models (autoencoders, GCNN, BERT-like, word2vec, and MAT) against traditional ECFP fingerprints for compound similarity search. Using vector databases, the study finds that Continuous Data-Driven Descriptors (CDDD) and MolFormer significantly improve search efficiency and speed, contributing to advancements in drug discovery methodologies.

12 Apr 2025

  • Molecular insights on the pre-reactive complex between SHAPE probe and RNA molecules and its role in the SHAPE reaction: a multiscale study
    Elisa Frezza, Cécilia Hognon, Ameni Ben Abdeljaoued, Pierre Hardouin, Mélanie Etheve-Quelquejeu, Bruno Sargueil, Damien Laage
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-11
     The paper investigates the interactions between SHAPE probes and RNA molecules using biased molecular dynamics and quantum mechanics/molecular mechanics simulations on the GAAA RNA tetraloop. It reveals how local structural environments influence SHAPE reactivity, emphasizing the pre-reactive complex’s binding mode and the importance of accurate environmental conditions for effective SHAPE reaction accommodation.
  • Role of Active Site Residues and Weak Noncovalent Interactions In Substrate Positioning in N,N-Dimethylformamidase
    Clorice Reinhardt, David Kastner, Heather Kulik
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-11
     The paper investigates the role of active site residues and noncovalent interactions in substrate positioning for the enzyme N,N-dimethylformamidase (DMFase) using molecular dynamics (MD) simulations and docking studies. Key findings include DMF binding stabilization through interactions with phenylalanine residues, identification of crucial active site residues, and insights into the structure of putative DMFases for bioremediation.
  • Dual Organelle Targeting for Intra-Organelle Click: Mitochondria and Endoplasmic Reticulum-Directed Benzothiophene-Fused Cycloalkyne Probes
    Natalia A. Danilkina, Aleksandra A. Vidyakina, Sergey A. Silonov, Alexander Y. Ivanov, Elena A. Shpakova, Ekaterina P. Podolskaya, Mia D. Kim, Alexey S. Gladchuk, Irina A. Balova, Stefan Bräse
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-11
     The paper presents dual organelle-targeted cycloalkyne probes for mitochondria and endoplasmic reticulum. The probes, based on benzothiophene-fused azacyclononyne, utilize a mitochondrial targeting group and a sulfonamide for ER delivery. Fluorescence microscopy confirmed their localization and intra-organelle click reactions with azides. The results highlight effective dual targeting for organelle studies and drug delivery.
  • A Multi-Task Learning Approach for Data Imputation of Compound Bioactivity Values for the SLC Transporter Superfamily
    Tarik Cerimagic, Sergey Sosnin, Gerhard Ecker
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-11
     The paper presents a multi-task deep neural network (MTDNN) designed for imputing missing compound-bioactivity values for SLC transporters, which have a data density of 2.53%. The model achieved an R² of 0.74, imputing values for 9,122 compounds across 54 targets, improving predictive accuracy compared to single-task learning methods.
  • Structure-Based Discovery of a Cryptic Druggable Pocket in TP53 C238Y: Implications for Targeted Therapy
    hoosdally shakeel
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-11
     This paper discusses the discovery of a cryptic druggable pocket in the TP53 C238Y mutation through computational modeling and structural analysis. This structure change could allow for targeted cancer therapies that address specific TP53 mutations. The study sets the stage for future virtual screening and drug-binding validation, emphasizing the potential for personalized cancer treatments.
  • Assessing Campylobacter jejuni Extracellular Vesicle-Host Interaction Using a Microfluidic Platform with Caco-2 Spheroides-on-Chip
    Silvia Tea Calzuola, Debora Pinamonti, Francesco Rizzotto, Jeanne Malet-Villemagne, Céline Henry, Christine Péchaux, Jean-Baptiste Blondé, Emmanuel Roy, Marisa Manzano, Goran Lakisic, Sandrine Truchet, Jasmina Vidic
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-11
     This paper presents a microfluidic platform with an impedimetric sensor to study the interactions between Campylobacter jejuni extracellular vesicles (EVs) and Caco-2 intestinal epithelial cells. The platform demonstrated that 3D Caco-2 spheroids were more resistant to C. jejuni EVs compared to 2D cultures, suggesting improved physiological relevance for studying host-microbe interactions in gastroenteritis.
  • Mechanism of Ag+-induced Folding of a Bacterial Peptide from Replica-Exchange Molecular Simulations
    Florian E.C. Blanc, Maggy Hologne, Mélodie Demontrond, Henry Chermette, Olivier Walker
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-11
     The paper investigates how Ag+ induces folding in the B1 peptide from the SilE protein using molecular simulations, NMR, and deep learning. Methods included DFT for interaction parametrization and replica-exchange simulations. Results show Ag+ promotes helix formation by affecting entropy and electrostatic stabilization, enhancing understanding of metal-induced protein folding and implications for bacterial silver resistance.
  • Generative protein design meets synthetic porphyrin assembly
    Hiroaki Inaba, Hiroki Onoda, Takayuki Uchihashi, Atsunori Oshima, Osami Shoji
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-11
     The paper presents BiPAD, a novel artificial protein assembly combining de novo protein design and synthetic supramolecular strategies. Utilizing generative protein design, BiPAD captures two synthetic porphyrins, achieving a metal-responsive cyclic assembly. High-speed atomic force microscopy demonstrated dynamic structural changes, showcasing new possibilities for functional protein assemblies.

11 Apr 2025

  • MolSculptor: a diffusion-evolution framework for multi-site inhibitor design
    Yanheng Li, Xiaohan Lin, Yize Hao, Jun Zhang, Yi Qin Gao
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-10
     The paper presents MolSculptor, a training-free framework for designing multi-site inhibitors using a latent diffusion model and evolutionary algorithms. It addresses the limitations of current deep generative models due to scarce training datasets. MolSculptor showed superior performance in various tasks, including optimizing dual-target inhibitors and selective inhibitor design, compared to existing methods.
  • A Multi-Task Learning Approach for Data Imputation of Compound Bioactivity Values for the SLC Transporter Superfamily
    Tarik Cerimagic, Sergey Sosnin, Gerhard Ecker
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-10
     The paper presents a multi-task deep neural network (MTDNN) for imputing missing compound-bioactivity values for the SLC transporter superfamily. Using a dataset with 2.53% density, the model predicted 480,133 values for 9,122 compounds across 54 targets, achieving an R² of 0.74. The approach outperformed single-task methods and shows promise despite data limitations.
  • Kinetic predictions for SN2 reactions using the BERT architecture: Comparison and interpretation
    Chloe Wilson, María Calvo, Stamatia Zavitsanou, James Somper, Ewa Wieczorek, Tom Watts, Jason Crain, Fernanda Duarte
    Organic Chemistry on ChemRxiv
    2025-04-10
     The paper investigates the use of BERT to predict logk values for SN2 reactions, comparing its accuracy to a top-performing Random Forest model. Both models achieved near-experimental accuracy (RMSE = 1.1 logk units) but showed limitations in extrapolation and recognition of effects. The study highlights the potential of BERT in kinetic prediction while addressing model interpretation.
  • Understanding cation and surface charging effects at electrified interfaces using neural network interatomic potentials
    Nitish Govindarajan, Jiawei Guo, Marcos Calegari Andrade, Christopher Hahn, Ambarish Kulkarni
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-10
     The paper explores electrified interfaces using message-passing neural network interatomic potentials based on the MACE architecture to conduct molecular dynamics simulations. It investigates the effects of surface charging and cation identity (Li+, Na+, K+, Cs+) on interfacial properties, revealing that surface charging significantly influences dynamics and structure, while double layer capacitance (17-19 µF/cm²) remains largely insensitive to cation types.
  • BigSolDB 20: a dataset of solubility values for organic compounds in organic solvents and water at various temperatures
    Lev Krasnov, Dmitry Malikov, Marina Kiseleva, Sergei Tatarin, Stanislav Bezzubov
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-10
     The paper presents BigSolDB 20, a dataset with 103,944 solubility values for 1,448 organic compounds in 213 solvents, measured across temperatures from 243 to 425 K. The data is standardized and accessible in a machine-readable format, supporting data-driven analyses. A web-tool for visualization is also introduced, aiming to aid machine learning in solubility prediction.
  • A consistent set of thermophysical properties of methane curated with machine learning
    Itamar Borges Jr, Matheus Maximo-Canadas, Rubens Caio Souza, Julio Cesar Duarte, Jakler Nichele, Leonardo Santos de Brito Alves, Luis Octavio Vieira Pereira, Ligia Gaigher Franco
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-10
     The paper presents a machine learning approach to accurately predicting methane’s thermophysical properties across various phases, utilizing a range of ML algorithms. The method aligns predictions more closely with standardized NIST data compared to raw experimental data, highlighting ML’s effectiveness in managing noise and variability. Extra Trees and Gradient Boosting were particularly noted for their scalability and efficiency.
  • Optimizing mixtures of metal–organic frameworks for robust and bespoke passive atmospheric water harvesting
    Cory Simon, Qia Ke, Charles Harriman, Ashlee Howarth, Thijs Vlugt
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-10
     The paper presents a method for optimizing mixtures of metal-organic frameworks (MOFs) for passive atmospheric water harvesting (AWH), using historical weather data and thermodynamic models in a linear programming framework. Case studies in the Chihuahuan and Sonoran Deserts reveal that mixed-MOF beds can be more effective than single-MOF beds, with optimal compositions varying by location and time.

10 Apr 2025

  • Mitochondria-targeting abasic site-reactive probe (mTAP) enables the manipulation of mitochondrial DNA repair and turnover
    Anal, Jana; Yu-Hsuan, Chen; Linlin, Zhao
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-09
     The study introduces mTAP, a mitochondria-targeting probe that selectively reacts with mitochondrial abasic sites, manipulating mtDNA repair. Utilizing mass spectrometry, it confirmed mTAP’s specificity and demonstrated its ability to prevent AP endonuclease binding. Cellular tests showed mTAP improved mtDNA copy number and transcription after AP site damage, revealing its potential in mitochondrial DNA maintenance.
  • Exploring a Druggable Hydrophobic Tunnel in the 5-HT2A Receptor with Potent Phenethylamines
    Christian B. M., Poulie; Icaro A., Simon; Eline, Pottie; Kasper, Harpsøe; Anders A., Jensen; Jesper L., Kristensen; Christophe P., Stove
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-09
     The paper investigates a hydrophobic tunnel in the 5-HT2A receptor implicated in its pharmacology. The study used molecular modeling and synthesized ten phenethylamine analogs to explore agonist activity. Key residues were identified, revealing that analogs with specific 4-substituents enhance agonist potency through tunnel interactions, marking the tunnel as a potential target for drug design.
  • Glutathione-Nucleotide Supramolecular Hydrogels with Intrinsic Antiviral, Antibacterial, and Anticancer Activities
    Tridib K., Sarma; Vidhi, Agarwal; Vaishali, Saini; Aditya, Prasun; Nidhi, Varshney; Amrita, Chakraborty; Hem Chandra, Jha
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-09
     The paper presents glutathione-based hydrogels formed by guanosine monophosphate, exhibiting antiviral, antibacterial, and anticancer properties. The hydrogels maintain their structure through physical crosslinking, showing stability and biocompatibility. In vitro studies demonstrate antiviral efficacy against EBV and SARS-CoV-2, as well as antibacterial effects. Results suggest potential applications in antimicrobial coatings and therapeutic solutions for COVID-19-related concerns.
  • Advanced computational methods in protein simulations  A case study of enhanced sampling applied to membrane transporters
    Philip, Biggin; Jonathan, Colburn; Simon, Lichtinger
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-09
     The paper reviews enhanced sampling techniques in molecular dynamics simulations, focusing on their application to membrane transporters. It categorizes problems into conformational changes and intermolecular interactions. The authors anticipate future developments, including machine learning integration, and recommend standardized reporting for better reproducibility. No specific datasets or results are mentioned in the abstract.
  • Reaction Exploration Reveals Strong Kinetic Filtering in Li-Ion Battery Electrolyte Degradation
    Brett, Savoie; Hsuan-Hao, Hsu; Tianfan, Jin
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-09
     The paper investigates the degradation reactions of lithium-ion battery electrolytes, focusing on the solid electrolyte interphase (SEI). Using automated reaction exploration methods, it analyzes the traditional electrolyte of ethylene carbonate and lithium hexafluorophosphate, revealing strong kinetic filtering effects and key degradation products like lithium ethylene monocarbonate and lithium ethylene dicarbonate. The study emphasizes the utility of computational methods for understanding SEI formation.
  • Discriminant Analysis Optimizes Progress Coordinate in Weighted Ensemble Simulations of Rare Event Kinetics
    Praveen Ranganath, Prabhakar; Dhiman, Ray; Ioan, Andricioaei
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-09
     The paper presents a machine learning approach using harmonic linear discriminant analysis to optimize progress coordinates in weighted ensemble simulations, enhancing the computation of rare biomolecular conformational transitions. Tested on alanine dipeptide kinetics and small protein unfolding, this method requires minimal system knowledge, potentially broadening its applicability.
  • Hydrogen Bonds under Electric Fields with Quantum Accuracy
    Giuseppe, Cassone; Alessandro , Amadeo; Marco Francesco, Torre; Klaudia, Mràziková; Franz, Saija; Sebastiano, Trusso; Jing, Xie; Matteo, Tommasini
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-09
     The study examines hydrogen bonds in dimers (H2O, HF, H2S, NH3) under electric fields using correlated quantum methods (CCSD and CCSD(T)). It reveals that electric fields strengthen intermolecular interactions and modifies H-bond properties, with the vibrational Stark effect linked to binding energies. Findings have implications for catalysis and biological processes, highlighting variations based on molecular polarizability.
  • Integrating Pharmacokinetics and Quantitative Systems Pharmacology Approaches in Generative Drug Design
    Helle W., van den Maagdenberg; Jikke, de Mol van Otterloo; Piet H., van der Graaf; J. G. Coen, van Hasselt; Gerard J. P., van Westen
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-09
     The paper presents an integrated framework combining pharmacokinetics (PK) and pharmacodynamics (PD) in generative drug design using DrugEx. It utilizes quantitative structure-property relationship models to predict PK properties and receptor affinity. The results indicate that optimizing these parameters can influence molecular scaffolds and expected tumor growth inhibition, suggesting a new approach for advanced drug design.
  • Beyond Lithium Lanthanum Titanate: Metal-Stable Hafnium Perovskite Electrolytes for Solid-State Batteries
    Basant, Ali; Charles, Musgrave; Ahmed, Biby
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-09
     The paper investigates hafnium perovskite electrolytes (LLHfO) as alternatives to lithium lanthanum titanate (LLTO) for solid-state batteries, emphasizing their stability against metal anodes. Using DFT calculations, the study reveals that LLHfO offers mechanical stability and lower diffusion barriers compared to LLTO. Sodium perovskites are noted for stability but require improved Na+ diffusion for better ionic conductivity.
  • Preorganized electric fields in voltage-gated sodium channels
    Valerie, Vaissier Welborn; Yi, Zheng; Taoyi, Chen
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-09
     The paper investigates electric fields in sodium channels using molecular dynamics simulations of Nav1.5, Nav1.6, and Nav1.7 with the AMOEBA polarizable force field. It finds that both charged and uncharged residues create significant electric fields facilitating Na+ movement, stressing the role of charge-dipole interactions and highlighting mutual information among residues in optimizing allosteric pathways.
  • Constructing Accurate Potential Energy Surfaces with Limited High-Level Data Using Atom-Centered Potentials and Density Functional Theory
    Mahsa, Nazemi Ashani; Alberto , Otero-de-la-Roza; Gino , A. DiLabio
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-09
     The paper presents a method for creating accurate potential energy surfaces (PESs) using limited high-level reference data. The authors employ a quasirandom sampling approach and fit atom-centered potentials (ACPs) to enhance density functional theory (DFT) methods. Results show significant RMSE reductions for HFCO and uracil molecules, demonstrating the effectiveness of the technique with minimal data points.

09 Apr 2025

08 Apr 2025

  • Polyglycerol-based Lipids: A Next-Generation Alternative to PEG in Lipid Nanoparticles for Advanced Drug Delivery Systems
    Yara Ensminger, Rashmi Rashmi, Gideon Nölte, Michael Karimov, Ann-Cathrin Schmitt, David Diaz-Oviedo, Johannes Köbberling, Rainer Haag, Markus Hafke
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-07
     The study explores linear polyglycerol (lPG) as an alternative to polyethylene glycol (PEG) in lipid nanoparticles (LNPs) for drug delivery. It demonstrates that PG-functionalized LNPs avoid binding to anti-PEG antibodies and effectively deliver mRNA, achieving transfection efficacy similar to PEGylated LNPs, highlighting a promising advancement in drug delivery systems.

  • Structure-Based Discovery of a Cryptic Druggable Pocket in TP53 C238Y: Implications for Targeted Therapy
    hoosdally shakeel
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-07
     The paper investigates the C238Y mutation in TP53, a gene often associated with cancer, using computational modeling and structural biological analysis. The study identifies a previously hidden druggable pocket created by this mutation, suggesting the potential for targeted therapies. Results underline the importance of mutation-specific approaches in developing personalized cancer treatments.

  • Dual Organelle Targeting for Intra-Organelle Click: Mitochondria and Endoplasmic Reticulum-Directed Benzothiophene-Fused Cycloalkyne Probes
    Natalia A. Danilkina, Aleksandra A. Vidyakina, Sergey A. Silonov, Alexander Y. Ivanov, Elena A. Shpakova, Ekaterina P. Podolskaya, Mia D. Kim, Alexey S. Gladchuk, Irina A. Balova, Stefan Bräse
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-07
     The paper presents dual organelle-targeted cycloalkyne reagents for mitochondria and endoplasmic reticulum (ER) using the benzothiophene-fused azacyclononyne BT9N. The study employs fluorescence microscopy for localization and quantifies co-localization. Results confirm efficient intra-organelle click reactions with azides, showcasing potential for organelle-targeted studies and drug delivery.

  • Discovery of Novel EGFR Kinase Inhibitors Using a Wild-Type EGFR: A Computational Approach
    Dhrubajyoti Maji
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-07
     The study presents a computational approach to discover novel EGFR kinase inhibitors using a pharmacophore model based on the 3D structure (PDB ID: 4WKQ). The model screened a dataset and the ChEMBL library, yielding 25,000 candidates. Four lead compounds were identified with superior binding affinities, and stability was confirmed through molecular dynamics, validating this robust pipeline for kinase-targeted drug discovery.

  • Theoretical Framework for Absolute Quantification in Digital Immunoassays and Advancements in Microfluidic-Free Droplet-Based Digital Immunoassay Methodologies
    Huan Li, Tianjiao Mao, Liang Lu, Yipi Xiao, Fanrong Ai, Liang Guo, Jiani Wang, Jing Yao, Xiluan Yan
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-07
     The paper presents a new theoretical framework for absolute quantification in digital immunoassays, modeling protein capture using magnetic beads as Poisson processes. It introduces bead-counting and external calibration methods that simplify traditional calibration processes. Comparative analyses show improved performance over existing methods. The research enhances protein quantification techniques in biomedical fields.

  • A general and accessible approach to enrichment and characterisation of natural anti-Neu5Gc antibodies from human samples
    Martin Fascione, Nathalie Signoret, Esme Hutton, Yumiko Uno, Craig Robson
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-07
     The paper presents a simplified method for enriching and detecting anti-Neu5Gc antibodies from small human serum samples using an affinity purification followed by ELISA. The approach utilizes CMAH-transfected human cells for Neu5Gc-containing glycans and successfully identified antibodies in all tested donors, demonstrating individual reactivity differences while ensuring effective binding to Neu5Gc glycans on whole human cells.

  • Kinetic predictions for SN2 reactions using the BERT architecture: Comparison and interpretation
    Chloe Wilson, María Calvo, Stamatia Zavitsanou, James Somper, Ewa Wieczorek, Tom Watts, Jason Crain, Fernanda Duarte
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-07
     This paper explores the use of the BERT architecture to predict logk values for SN2 reactions, comparing its performance against Random Forest models. Both achieved RMSE = 1.1 logk units on similarity-split test data, reflecting high accuracy. However, BERT struggled with aromatic effect recognition while RF had limitations in logk extrapolation.

  • Ligand Controlled Regioselectivities in C–H Activation - A Case Study on Approaches for the Rationalization and Prediction of Selectivities
    Manuel van Gemmeren, Fritz Deufel
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-07
     The paper investigates ligand effects on regioselectivity in Pd-catalyzed alkynylation of thiophenes. Using multivariate linear regression and DFT studies, it identifies key structural factors and proposes a Curtin-Hammett scenario for selectivity. The study highlights solvent interactions and silver’s role in enhancing selectivity, offering insights for future research in C–H activation mechanisms.

  • Twenty Years After: Scaling Relations in Oxygen Electrocatalysis and Beyond
    Nadezda Kongi, Vladislav Ivaništšev, Ritums Cepitis, Jan Rossmeisl
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-07
     The paper reviews scaling relations in oxygen electrocatalysis, especially regarding the oxygen reduction reaction (ORR). It analyzes existing literature, proposes new theoretical principles, and categorizes strategies to overcome scaling relations that limit catalyst effectiveness. The paper aims to reshape the understanding and approaches to ORR catalysis challenges, particularly for fuel cells and metal-air batteries.

  • Autonomous Discovery of Polymer Electrolyte Formulations with Warm-start Batch Bayesian Optimization
    Jurgis Ruza, Michael Stolberg, Sawyer Cawthern, Jeremiah Johnson, Yang Shao-Horn, Rafael Gomez-Bombarelli
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-07
     The paper presents a closed-loop Bayesian optimization pipeline for discovering and optimizing polymer electrolyte formulations using poly(\epsilon-caprolactone) and 18 lithium salts. Utilizing literature data for warm-starting, the method achieved high-performance results, discovering formulations with ionic conductivity comparable to leading poly(ethylene oxide) electrolytes within five batches over a month on a high-throughput platform.

05 Apr 2025

  • From GPCRs to Kinases: Automating the Path to Universal Pandemic Vaccine Development using Artificial Intelligence
    Asif Sumbul, Mukherjee Debaprasad
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-04
     The paper discusses the urgent need for universal pandemic vaccines targeting COVID-19 and related coronaviruses, emphasizing the roles of GPCRs and kinases in viral entry and replication. It highlights the application of AI, machine learning, and bioinformatics in automating vaccine development, underscoring the importance of these methods in addressing emerging viral variants.
  • Discovery of histone deacetylase 8 (HDAC8)-specific proteolysis-targeting chimeras with anti-cancer activity against hematological malignancies
    Finn Kristian Hansen, Shiyang Zhai, Marie Kemkes, Cindy-Esther Kponomaizoun, Felix Feller, Jia-Wey Tu, Dominika Ewa Pieńkowska, Jan Gerhartz, Julian Schliehe-Diecks, Michael Gütschow, Radosław P. Nowak, Christian Steinebach, Sanil Bhatia
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-04
     This study develops two series of cereblon-recruiting PROTACs for selective HDAC8 degradation, using HDAC8 inhibitor PCI-34051. The pomalidomide/thalidomide-based series (BP1-05) showed strong anti-leukemic activity and CRBN substrate degradation, while the phenyl glutarimide series (BP6-010) displayed low cytotoxicity but enhanced stability. BP6-treated cells increased sensitivity to MEK inhibitor cobimetinib.
  • Non-Noyori-type Ruthenium-POP Pincer Catalysts in Ethanol Upgrading
    Martin Nielsen, Alexander Tobias Nikol, Rosa Padilla
    Organic Chemistry on ChemRxiv
    2025-04-04
     The paper reports the synthesis of new Ruthenium-POP pincer complexes for ethanol upgrading to butanol. Using tridentate ligands, the catalysts showed varying analytical properties based on electronic and steric factors. Results indicated a maximum yield of 28% 1-butanol at 120 °C over 48 hours.
  • Dual Nickel-Photoredox Catalyzed Amidine-Arylation Method Mediated By In Situ Generated Triazine Cocatalyst
    Gellért Sipos, Matteo Gasparetto, Attila Sveiczer, Andrea Fermi, Mounir Raji, Richard J. Fair, Paola Ceroni
    Organic Chemistry on ChemRxiv
    2025-04-04
     The paper presents a nickel/photoredox-catalyzed method for amidine arylation with high yields across various substrates, demonstrating good functional group tolerance. Key findings include the role of an in situ generated triazine cocatalyst, leading to optimized reaction rates and milder conditions.
  • Deoxygenative Functionalization of Alcohols and Carbonyl Compounds via Electrochemical Reduction
    Andrew Ressler, Jesus Martinez-Alvarado, Ruchira Hariharan, Weiyang Guan, Song Lin
    Organic Chemistry on ChemRxiv
    2025-04-04
     The paper presents a method for deoxygenating alcohols and carbonyl compounds using hydrosilanes to create silyl ether intermediates, followed by electrochemical reduction for C–O bond cleavage. This strategy allows for the synthesis of various carbon bonds (C–Si, C–B, C–Ge, C–Sn) from these intermediates.
  • Accelerated investigation of complex reaction cascades via digital aerosol chemistry coupled to online mass spectrometry
    S. Hessam M. Mehr, Zehua Li
    Organic Chemistry on ChemRxiv
    2025-04-04
     The paper presents a novel platform integrating aerosol chemistry and online mass spectrometry to enhance the observation of self-assembly processes. It addresses challenges in monitoring fleeting intermediates and insoluble products. The method’s effectiveness is illustrated through three case studies: polymerization, Schiff base synthesis, and covalent organic framework self-assembly.
  • Extending the MMPBSA method to membrane proteins: Addressing conformational changes upon ligand binding to P2Y12R
    Cizhang Zhao, Tianhong Wang, Ray Luo
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-04
     The paper discusses extending the MMPBSA method for membrane proteins, specifically addressing conformational changes in P2Y12R during ligand binding. It highlights advancements in Amber for calculating membrane placement parameters and multi-trajectory methods, emphasizing improvements in binding affinity calculations. The work reflects ongoing progress in computational techniques relevant to drug discovery involving membrane proteins.
  • Teaching chemists to code with diversity in mind: a pedagogy of belonging for end-user conditions
    Marie van Staveren
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-04
     The paper discusses how coding instruction for chemistry students should consider equity to enhance chemistry identity. It suggests inclusive teaching methods to cultivate a sense of belonging in a stereotypically exclusive field. No specific datasets or quantitative results are mentioned.

04 Apr 2025

  • N-isopropylacrylamide based nanogels crossing the blood-brain barrier: evidence of high in vitro internalization in human endothelial cells and in vivo permeation in zebrafish
    Marina Resmini, Roberta Bilardo, Adele Leggieri, Federico Traldi, Francesca Tomatis, Miguel Lino, Eleonora Rizzi, Caroline Mysiorek, Lino Ferreira, Caroline Brennan, Alena Vdovchenko
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-03
     The paper reports the synthesis of N-isopropylacrylamide nanogels that significantly enhance blood-brain barrier (BBB) permeability, achieving a 13.4% permeation in an in vitro human model. The nanogels, stable in zebrafish without toxicity, showed nearly five-fold improved internalization via clathrin-mediated endocytosis. Methods included fluorescent labeling for tracking and testing in both cell culture and zebrafish models.
  • Polyglycerol-based Lipids: A Next-Generation Alternative to PEG in Lipid Nanoparticles for Advanced Drug Delivery Systems
    Yara Ensminger, Rashmi Rashmi, Gideon Nölte, Michael Karimov, Ann-Cathrin Schmitt, David Diaz-Oviedo, Johannes Koebberling, Rainer Haag
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-03
     The paper explores polyglycerol-based lipids as a substitute for polyethylene glycol (PEG) in lipid nanoparticles (LNPs) for drug delivery. It demonstrates that polyglycerol-functionalized LNPs exhibit low binding to anti-PEG antibodies and effectively deliver eGFP mRNA into HepG2 cells, matching the transfection efficacy of PEGylated LNPs.
  • GlycoFASP: A Universal Method to Prepare Complex Mixtures for O-Glycoproteomic Analysis
    Stacy Malaker, Shane Finn, Keira Mahoney, Taryn Lucas, Valentina Rangel-Angarita, Ryan Chen
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-03
     The paper introduces GlycoFASP, a rapid and cost-effective method for O-glycoproteomic analysis using molecular-weight cut-off filters and specific O-glycoprotease digestion. It requires only 1 mg of sample, depletes non-O-glycosylated proteins effectively, and yields over 70% of protein signals from O-glycoproteins, making it accessible for non-specialists.
  • bis(trifluoromethyl)-carborhodamines: highly fluorogenic, far-red to near infrared dyes for live cell fluorescence microscopy, activity-based sensing, and single-molecule microscopy
    Evan Miller, Nels Gerstner, Jack McCann, Julia Martin, Sathvik Anantakrishnan, Katharine Henn, Kathrin Riske, Thomas Graham, Xavier Darzacq
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-03
     The paper introduces bis(trifluoromethyl)carborhodamine (BF) dyes, which have excitation and emission properties over 650 nm. These dyes exhibit high brightness and 30-fold increased fluorogenicity compared to traditional rhodamines. The study includes design, synthesis, and characterization of BF dyes, demonstrating their application in live cell fluorescence microscopy, including single-molecule tracking and no-wash labeling.
  • High Throughput Ligand Dissociation Kinetics Predictions using Site-Identification by Ligand Competitive Saturation
    Alex MacKerell, Wenbo Yu, Shashi Kumar, Mingtian Zhao, David Weber
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-03
     The paper presents a machine learning approach, SILCS-Kinetics, to predict ligand dissociation kinetics (koff) using 329 ligands across thirteen proteins. It combines the SILCS method for calculating free energy profiles with ML models to enhance predictions and reduce computational costs. Results demonstrate robust workflows for evaluating ligand dissociation pathways, aiding drug design efficiency.
  • MolPrice: Assessing Synthetic Accessibility of Molecules based on Market Value
    Friedrich Hastedt, Klaus Hellgardt, Sophia Yaliraki, Dongda Zhang, Antonio del Rio Chanona
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-03
     The paper presents MolPrice, a machine learning model for predicting molecular market prices to assess synthetic accessibility. It uses self-supervised contrastive learning to autonomously label complex molecules, outperforming existing models by reliably identifying purchasable molecules. Results indicate that MolPrice effectively distinguishes molecular complexity and performs well against state-of-the-art methods, enhancing real-world applicability in molecular design.
  • The density-based many-body expansion for polypeptides and proteins
    Christoph R. Jacob, Toni M. Maier, Johannes R. Vornweg
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-03
     The paper introduces a fragment-based method enhancing the quantum-chemical treatment of large biomolecular systems, specifically polypeptides and proteins, by combining the MFCC scheme with the density-based many-body expansion. This approach reduces fragmentation error to approximately 1 kJ/mol per amino acid, improving accuracy across various structural motifs.
  • A Benchmark Set of Bioactive Molecules for Diversity Analysis of Compound Libraries and Combinatorial Chemical Spaces
    Alexander Neumann, Raphael Klein
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-03
     The paper presents a benchmark set of bioactive molecules for evaluating compound libraries, utilizing the CHEMBL database to create three sets (Set L: 379k, Set M: 25k, Set S: 3k). The study employed methods including FTrees, SpaceLight, and SpaceMACS, finding that commercial Chemical Spaces outperformed enumerated libraries, offering more similar compounds and unique scaffolds.
  • Exploring organic chemical space for materials discovery using crystal structure prediction-informed evolutionary optimisation
    Graeme Day, Jay Johal
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-03
     The paper introduces CSP-EA, an evolutionary algorithm that integrates crystal structure prediction into the search for organic molecular semiconductors. By assessing candidate molecules based on predicted material properties, CSP-EA enhances performance over traditional methods focusing solely on molecular properties. The study demonstrates this improved approach through a case study on organic semiconductors, achieving higher identification of molecules with elevated electron mobilities.
  • DNA sequencing with a nanopore and a sub-nanometer stop-and-go positioning system for incremental base identification
    G. Sampath
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-03
     The paper presents a nanopore sequencing technique for single-stranded DNA using a sub-nanometer precision positioning system. It proposes thread-based movement and blockade current measurement to identify bases incrementally. The method requires a blockade current precision analysis for biological and synthetic pores and hints at potential applications for detecting DNA post-translational modifications.
  • Potential energy surfaces of bound and metastable electron-attached states of N2O characterized by a joint experimental and theoretical study
    Madhubani Mukherjee, Carson Baker, Miloš Ranković, Pamir Nag, Juraj Fedor, Anna I. Krylov
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-03
     The paper investigates electron scattering from nitrous oxide (N2O) using experimental two-dimensional electron energy loss spectra (EELS) at 2.5-2.6 eV and complex-valued equation-of-motion coupled-cluster theory. It identifies two overlapping resonances at 2.8 eV and 2.3 eV and estimates the electron affinity of N2O to be -0.140 eV, with a predissociative 2A′ state and a dissociation barrier of 0.183 eV.

03 Apr 2025

  • Efficient Decoy Selection to Improve Virtual Screening Using Machine Learning Models
    Oliver Koch, Janosch Menke, Victoria-Munoz Felipe, Norberto Sanchez-Cruz
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-02
     The paper investigates decoy selection strategies to improve machine learning models using Protein per Atom Score Contributions Derived Interaction Fingerprint (PADIF) for virtual screening in drug discovery. It explores three workflows for selecting decoys, including random selection from ZINC15 and utilizing dark chemical matter. Results indicate these methods enhance model performance and improve active compound selection over classical scoring functions.
  • Artificial Intelligence and Automation in Universal Pandemic Vaccine Design: A Strategic Imperative for Global Health Security
    Malika Ahuja, Debaprasad Mukherjee
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-02
     The paper discusses the urgent need for universal pandemic vaccines to combat evolving viruses and emphasizes the role of automation, including AI and robotics, in their development. It examines current technologies, challenges, and ethical considerations while highlighting ongoing research. The findings stress the importance of investment in automated vaccine design for global health security.
  • Setting New Benchmarks in AI-driven Infrared Structure Elucidation
    Marvin Alberts, Federico Zipoli, Teodoro Laino
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-02
     The paper presents enhancements in automated infrared (IR) structure elucidation using an improved Transformer architecture, achieving Top-1 accuracy of 63.79% and Top-10 accuracy of 83.95%. These results surpass previous benchmarks (53.56% and 80.36%). Models and code are publicly shared to promote adoption in chemical laboratories, emphasizing the potential of AI-driven IR spectroscopy.
  • BigSolDB 20: a dataset of solubility values for organic compounds in organic solvents and water at various temperatures
    Lev Krasnov, Dmitry Malikov, Marina Kiseleva, Sergei Tatarin, Stanislav Bezzubov
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-02
     The paper introduces BigSolDB 20, a dataset with 103,944 experimental solubility values for 1,448 organic compounds in 213 solvents, measured across 243-425 K. It standardizes molecular structures and solubility data in a machine-readable format. A web tool for visualization and search is provided, aiming to facilitate machine learning advancements in solubility prediction.
  • Automatic Annotation of Sites of Metabolism from Biotransformation Data
    Johannes Kirchmair, Roxane Axel Jacob, Angelica Mazzolari
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-02
     The paper presents AutoSOM, an open-source tool for automatically annotating sites of metabolism (SOM) from biotransformation data. It processes over 5,000 reactions with over 90% accuracy quickly and transparently. AutoSOM enhances SOM labeling consistency across institutions, supporting federated learning while maintaining data confidentiality, and aims to improve metabolic property studies in drug discovery.
  • Generative deep learning for de novo drug design – a chemical space odyssey
    Riza Özçelik, Helena Brinkmann, Emanuele Criscuolo, Francesca Grisoni
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-02
     The paper discusses the use of generative deep learning in drug design to explore chemical space and generate novel molecules. It highlights challenges in molecule generation and evaluation, focusing on balancing diversity, synthesizability, and bioactivity. The authors emphasize the need for robust evaluation protocols and outline key components and future directions in this evolving field.
  • Predicting Catalytic Activity for CH4 Combustion on Pd-exchanged Zeolite Catalysts Using Automated Reaction Route Mapping
    Shunsaku Yasumura, Shiho Sakuma, Kenichiro Saita, Tetsuya Taketsugu, Masaru Ogura
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-02
     The paper presents a novel approach combining neural network potentials and automated reaction route mapping to predict catalytic activity for CH4 combustion on Pd-exchanged zeolite catalysts (Pd-CHA, Pd-beta, Pd-MOR). Key findings include activation energies for the rate-determining step and the identification of intermediates, which align with experimental results and suggest efficient pre-screening for catalyst design.
  • Accessing Ultralarge Chemical Spaces via a Sociable Fragment Library: Design and Crystallographic Screening
    Oliver Koch, Philipp Janssen, Fabrice Becker, Friederike T. Füsser, Nataliya Tolmachova, Tetiana Matviiuk, Ivan Kondratov, Manfred Weiss, Daniel Kümmel, Laila Benz
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-02
     The paper presents a 96-membered “sociable” fragment library designed for crystallographic screening, enabling rapid exploration of chemical spaces. Utilizing Enamine’s REAL Space, it provides access to numerous follow-up compounds for each fragment. The library was successfully tested on mycobacterial thioredoxin reductase and Zika virus protease, leading to rapid experimental follow-up on promising fragments.
  • From short-sighted to far-sighted: A comparative study of recursive machine learning approaches for open quantum systems
    Arif Ullah
    Theoretical and Computational Chemistry on ChemRxiv
    2025-04-02
     This paper compares four physics-informed neural network (PINN) architectures for modeling open quantum systems: SR-PINN, PSR-PINN, MR-PINN, and PMR-PINN, applied to the spin-boson and Fenna-Matthews-Olson models. Results show that multi-RDMs-predicting models outperform single-RDMs in accuracy and stability for long-term predictions, revealing limitations in traditional short-sighted methods and the redundancy of including simulation parameters.

02 Apr 2025

  • From GPCRs to Kinases: Automating the Path to Universal Pandemic Vaccine Development
    Asif Sumbul, Mukherjee Debaprasad
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-01
     The paper discusses the critical roles of GPCRs and kinases in viral infections and the urgent need for universal pandemic vaccines against strains like Covid-19, SARS, and MERS. It highlights the potential of AI and computational biology in automating vaccine development, particularly for broad-spectrum antiviral treatments.

  • µMap-FFPE: A High-Resolution Protein Proximity Labeling Platform for Formalin-Fixed Paraffin-Embedded Tissue Samples
    Noah B. Bissonnette, Marie E. Zamanis, Steven D. Knutson, Zane Boyer, Angelo Harris, Daniel Martin, Jacob B. Geri, Suzana Couto, Tahamtan Ahmadi, Anantharaman Muthuswamy, Mark Fereshteh, David W. C. MacMillan
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-01
     The paper presents µMap-FFPE, a novel protein proximity labeling platform for studying protein interactions in formalin-fixed paraffin-embedded (FFPE) tissue samples. The method aims to explore microscale interactions critical for understanding disease states, specifically comparing CD20’s interactome between healthy and cancerous tissues, overcoming challenges posed by formalin-fixation.

  • Four new bright members of the ZinPyr zinc fluorescence sensor family for live cell imaging
    Sabine Becker, Marisa Franziska Jakobs, Max Carlsson, Simon Wittmann, Jörg Fahrer
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-01
     The paper introduces four new members of the ZinPyr zinc fluorescence sensor family—ZP1(5-en), ZP1(6-en), ZP1(5-Me2en), and ZP1(6-Me2en)—developed for live cell imaging. These sensors exhibit low quantum yields in the zinc-free state but high turn-on and dynamic ranges. They successfully detect intracellular zinc and localize in lysosomes and nuclei.

  • Computational Insights into the Binding Mechanism of L6I with Keap1 Kelch Domain (5FNU): A Molecular Docking Approach
    Swaroop Kumar . P
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-01
     The paper investigates the binding mechanism of the L6I inhibitor with the Keap1 Kelch domain (PDB ID: 5FNU) using molecular docking. The study utilized AutoDock to analyze binding affinity, achieving -10.6 kcal/mol with a binding distance of 2.677 Å. Results indicate L6I’s potential to disrupt Keap1-Nrf2 interaction, suggesting further experimental validation is needed.

  • High-performance quantitative exposomics covering up to >230 toxicants and key biomarkers
    Yunyun Gu, Max Lennart Feuerstein, Caroline Helen Johnson, Benedikt Warth
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-01
     The paper presents a scalable LC-MS/MS workflow for quantifying over 230 toxicants and biomarkers in urine, plasma, and serum. The method demonstrated high sensitivity, robustness, and suitable extraction recovery, with over 130 biomarkers detected in 200 urine samples from pregnant women. This approach supports large-scale exposome-wide association studies (ExWAS) effectively.

  • Polyglycerol-based Lipids: A Next-Generation Alternative to PEG in Lipid Nanoparticles for Advanced Drug Delivery Systems
    Yara Ensminger, Rashmi Rashmi, Gideon Nölte, Michael Karimov, Ann-Cathrin Schmitt, David Diaz-Oviedo, Johannes Koebberling, Rainer Haag
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-01
     The paper presents a study on polyglycerol-based lipid nanoparticles (LNPs) as an alternative to PEG for drug delivery, addressing issues with anti-PEG antibodies. The researchers formulated LNPs with linear polyglycerol (lPG) and demonstrated that these LNPs had low binding to anti-PEG antibodies and comparable mRNA delivery efficacy to PEGylated LNPs in HepG2 cells.

  • In-situ Dynamic Imaging of Magnetosome Biomineralization in A Living Bacterium
    Jiandong Feng, Qin Juan, Yibo Yang, Zengrong Zhou, Pinlong Zhao, Shiyang Lyu, Wenjing Duan, Hongzhen Bai, Jinhua Li
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-01
     The paper presents in-situ dynamic magnetic imaging of magnetosome biomineralization in living magnetotactic bacteria using Quantum-enabled Chemical Operando Microscopy. The study reveals a novel ‘retarded logistic type dynamics’ for magnetism development during magnetosome chain formation and elucidates the growth and assembly mechanisms at a sub-bacterium level, advancing the understanding of biomineralization in living organisms.

  • Structure-based identification of the first non-covalent thioredoxin reductase inhibitor with proven ADMET suitability
    Luisa Ronga, Giuseppe Felice Mangiatordi, Lamanna Giuseppe, Augello Giuseppa, Tesauro Diego, Silvestri Ilaria, Azzolina Antonina, Melchiorre Cervello, Saviano Michele
    Biological and Medicinal Chemistry on ChemRxiv
    2025-04-01
     The study identifies C55, the first non-covalent thioredoxin reductase 1 (TrxR1) inhibitor with established ADMET safety. Using a computational approach, over 90,000 compounds were screened, revealing C55’s micromolar-range inhibitory effect on TrxR1 across various cancer cell lines (HepG2, Huh7, MCF-7, MDA-MB-231), paving the way for anticancer drug development.

01 Apr 2025

  • Achieving excellent optical molecules by screening superalkali-doped cyclo[2n]carbon, M3O@C2n (M = Li, Na, and K, n = 5-10)
    Zeyu Liu, Wenwen Zhao, Jiaojiao Wang, Xiufen Yan, Tian Lu
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-31
     The paper explores nonlinear optical (NLO) molecules formed by superalkali-doped cyclo[2n]carbon (M3O@C2n, M = Li, Na, K; n = 5-10). Using time-dependent density functional theory, it finds that the polarizability and hyperpolarizability increase with alkali metal size. Specifically, Li3O@C20 shows exceptional properties, including a red-shifted absorption spectrum and potential for deep-ultraviolet NLO applications.

  • QRCI: Quantitative Ring Complexity Index for Evaluating Molecular Structure and Chemical Diversity
    Jianmin Wang, Kai Xu, Tengfei Ma, Xudong Zhang, Pengsen Ma, Chunyan Li, Weiran Huang, Meng Wang, Xiaojun Yao, Tao Song, Xiangxiang Zeng
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-31
     The paper introduces the Quantitative Ring Complexity Index (QRCI), which enhances traditional ring complexity assessments by incorporating ring diversity, topological complexity, and macrocyclic features. QRCI can be computed without 3D data and correlates with synthetic accessibility, aiding in evaluating molecular structures and chemical diversity for drug discovery amidst vast chemical spaces.

29 Mar 2025

  • Identification of Betulinic Acid Derivatives as Inverse Agonists of RAR-related Orphan Receptor Gamma (RORb3)
    Ya Chen, Patrik F. Schwarz, Alexander F. Perhal, Famke Guder, Jorge Enrique Hernández González, Kerrin Janssen, Ece Sağiroğlu, Ammar Tahir, Johannes Kirchmair, Natacha Rochel, Verena M. Dirsch
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-28
     The paper identifies novel betulinic acid derivatives as ROR-b3 inverse agonists, crucial for inhibiting Th17 cell differentiation linked to autoimmune diseases. A key compound exhibited IC50 values of 0.4 M and 0.6 M in ROR-b3 assays. RT-qPCR confirmed gene down-regulation, with structure-activity relationships explored through molecular docking and mutagenesis.

  • Cryo-EM exposes diverse polymorphism in IAPP mutants to guide rational design of novel therapeutics
    Dylan Valli, Saik Ann Ooi, Mikołaj Ignacy Kuska, Helena Marí, Himanshu Chaudhary, Weixiao Yuan Wahlgren, Sebastian Westenhoff, Alesia A Tietze, Anna Novials, Joan-Marc Servitja, Michał Maj
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-28
     The paper investigates IAPP mutants for type 2 diabetes therapies using cryo-electron microscopy to reveal six polymorphs from single-point mutations A25P, S28P, and S29P. Notably, the A25P variant formed a unique trimeric structure. Structures guided the design of novel non-amyloidogenic IAPP mutants, showcasing the potential of structure-based therapeutic development against amyloid diseases.

  • Ligand binding into a cryptic pocket in Escherichia coli DsbA inhibits the enzymatic activity in vitro
    Martin Scanlon, Biswaranjan Mohanty, Wesam Alwan, Menachem Gunzburg, Olga Ilyichova, Martin Williams, Gaurav Sharma, Geqing Wang, Begona Heras, Bryn Fenwick, Peter Wright, Pramodh Vallurapalli, Bradley Doak, Ben Capuano
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-28
     The paper identifies a cryptic ligand binding pocket in Escherichia coli DsbA using nuclear magnetic resonance (NMR) relaxation-dispersion data. It describes a dynamics-driven fragment discovery method and reveals that compounds bind to the pocket with slow kinetics, inhibiting DsbA’s activity in vitro. This suggests the pocket’s potential for developing new anti-virulence agents targeting EcDsbA.

  • Integrative Design of ADAMTS Partial Agonists within a Multi-Hallmark Therapeutic Framework: The Beginning of Paving the Way Toward a Functional Cure for Neurodegenerative Disorders
    David Ferguson, MRSB MRSC
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-28
     The paper discusses a multi-hallmark therapeutic approach to neurodegenerative disorders, focusing on ADAMTS metalloproteinases. It emphasizes bioisosteric design in developing partial agonists to control ECM degradation and reduce neuroinflammation. The integration of immune modulation and drug delivery systems is highlighted as enhancing treatment efficacy, aiming to restore functional neural networks in diseases like Alzheimer’s and Parkinson’s.

  • Smart sensor particles for intracellular tracing of reactive oxygen species by transmission electron microscopy
    Stephanie Hoeppener, Maren T. Kuchenbrod-Röther, Paul M. Jordan, Phillip Dahlke, Alicia De San Luis Gonzalez, Steffi Stumpf, Oliver Werz
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-28
     The paper presents a novel sensor particle system for detecting reactive oxygen species (ROS) within cells using Transmission Electron Microscopy (TEM). The method involves linking cerium chloride to poly(2-vinylpyridine) particles, reducing cytotoxicity and expanding cultivation media options. This approach enhances existing STAINING protocols for ROS detection.

28 Mar 2025

  • Binding modes of the KRAS(G12C) inhibitors GDC-6036 and LY3537982 revealed by all atom molecular dynamics simulations
    Tatu, Pantsar; Renne, Leini; Kari, Kopra; Jonas, Kapp
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-27
     The paper reveals the binding modes of KRAS(G12C) inhibitors GDC-6036 and LY3537982 using all-atom molecular dynamics simulations, totaling 200 microseconds. Experimental assays show high affinity for both inhibitors towards KRAS(G12C), but efficacy is reduced by certain co-mutations. The findings support the use of microsecond simulations for predicting binding interactions, aligning with experimental data.

  • Investigating errors in alchemical free energy predictions using random forest models and GaMD
    Skanda, Sastry; Michael, Kim
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-27
     The paper investigates alchemical free energy (ΔG) prediction errors using random forest models and GaMD for antibody-antigen complexes. It highlights that current predictions, while sufficient for screening, fail to assess critical post-translational modifications that can significantly impair binding. Key metrics include a ΔG threshold of +0.5 kcal/mol, indicating a 50% loss in dissociation constant (Kd).

27 Mar 2025

  • Leveraging High-Spin DFT Features for Prediction of Spin State Gaps in 3d Transition Metal Complexes
    Anuj Kumar, Ray; Sandeep , Nagar; Girish, Varma; U Deva, Priyakumar; Ankan, Paul
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-26
     The paper presents a machine learning approach to predict spin state energetics in 3d transition metal complexes using features from a single high-spin DFT calculation. Trained on 1434 SSE values from 934 complexes, the method retains accuracy while bypassing multi-reference optimizations, demonstrating transferability to more complex ligands. Key features include atomic energy levels and d-orbital eigenvalues.

  • Molecular aromaticity: a quantum phenomenon
    Miquel, Solà; Dariusz W. , Szczepanik
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-26
     The paper re-evaluates the classification of cyclo[18]carbon, a molecule identified in 2019, questioning its aromaticity based on quantum mechanics principles. The analysis suggests it should be considered non-aromatic. No specific datasets or experimental methods are mentioned in the abstract.

  • Predicting Catalytic Activity for CH4 Combustion on Pd-exchanged Zeolite Catalysts Using Automated Reaction Route Mapping
    Shunsaku, Yasumura; Shiho, Sakuma; Kenichiro, Saita; Tetsuya, Taketsugu; Masaru, Ogura
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-26
     The paper discusses predicting CH4 combustion catalytic activity using Pd-exchanged zeolites through automated reaction route mapping and neural network potential. Key findings include identifying intermediate species and evaluating activation energies, with Pd-MOR showing the highest catalytic activity. The research highlights the effectiveness of NNP in rationalizing catalyst design and pre-screening.

  • Generative deep learning for de novo drug design – a chemical space odyssey
    Rıza, Özçelik; Helena, Brinkmann; Emanuele, Criscuolo; Francesca, Grisoni
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-26
     The paper explores the use of generative deep learning in drug design, addressing challenges in molecule generation and evaluation. It highlights the importance of balancing chemical diversity, synthesizability, and bioactivity. The authors discuss current methods, optimization, and evaluation protocols, outlining future directions for utilizing generative models in exploring chemical space for novel drug development.

  • PolyMetriX: An Ecosystem for Digital Polymer Chemistry
    Kevin Maik, Jablonka; Sreekanth, Kunchapu
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-26
     The paper introduces PolyMetriX, an open-source Python library that standardizes and improves the digital polymer chemistry workflow. It provides curated polymer property datasets, hierarchical featurization techniques, and polymer-specific data splitting strategies. These enhancements lead to better model performance and reproducibility in polymer design, addressing issues of comparability in the field.

  • Active learning FEP using 3D-QSAR for prioritizing bioisosteres in medicinal chemistry
    Venkata Krishnan, Ramaswamy; Matthew, Habgood; Mark, Mackey
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-26
     The paper presents an active learning workflow combining 3D-QSAR and relative binding free energy calculations to prioritize bioisosteres in drug discovery. Demonstrated on a human aldose reductase case, this method efficiently identifies potent bioisosteric replacements from large datasets, achieving significant results with modest computational resources.

  • Machine learning-guided materials and system co-design for high-pressure hydrogen compression
    Matthew, Witman; Brendan, Davis; Vitalie, Stavila; Terry, Johnson
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-26
     The paper discusses enhancing hydrogen compression technology using machine learning for co-design of materials and systems. It presents a method for optimizing low-stability metal hydrides for high-pressure applications, validated through pressure-composition-temperature isotherms, and models compressor efficiency, predicting operational efficiencies for 2-stage and 3-stage compressors.

26 Mar 2025

  • Spatiotemporal Control of Formation of Dynamic Protein Fibre Networks via Photophysical Effects of a Focused Laser Beam
    Hiroshi Y. Yoshikawa, Ren Shirata, Takuya Takeshige, Fumika Kiryu, Kei Takano, Shuma Matsumoto, Natsumi Sawada, Chi-Shiun Wu, Yang-Hsin Shih, Hiromasa Niinomi, Takahisa Matsuzaki, Seiichiro Nakabayashi, Teruki Sugiyama, Ryuzo Kawamura
    https://chemrxiv.org/engage/rss/chemrxiv?categoryId=605c72ef153207001f6470d0
    2025-03-25
     The paper demonstrates a method for controlling the formation of dynamic microtubule networks using focused laser irradiation, which accumulates tubulin proteins at laser foci. The resulting networks exhibit dynamic behaviors like motion and cilia-like beating, driven by motor proteins. This technique relies on laser trapping and heat generation, enabling complex network fabrication without photochemical reactions, with potential applications in bioengineering.
  • Discovery of a Dual-Target Natural Compound for Rheumatoid Arthritis: High-Throughput Screening and Molecular Dynamics of a COX-2/JAK1 Inhibitor
    Dhrubajyoti Maji
    https://chemrxiv.org/engage/rss/chemrxiv?categoryId=605c72ef153207001f6470d0
    2025-03-25
     The paper reports the discovery of a dual-target compound, F3139-1037, for rheumatoid arthritis through high-throughput screening of 3666 natural compounds. This compound demonstrates superior binding affinity and inhibition for COX-2 and JAK1 compared to standard drugs. Molecular dynamics simulations confirm its stability, suggesting F3139-1037 as a promising candidate for multi-target RA therapies.
  • Counterintuitive Photochemistry of an Isolated Acridinyl Radical: ConPET via Preassembly Despite a Long-Lived Excited State
    Daniel Scott, Samuel Horsewill, Katherine Sharrock, P9r Feh9r, Jack Woolley, Imre P9pai
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-25
     The paper investigates the photochemistry of an isolated acridinyl radical in consecutive photoinduced electron transfer (conPET). Using photoreactivity, photoluminescence, and transient absorption techniques, it reveals that preassembly of the catalyst and substrate occurs prior to excitation, challenging previous assumptions about the role of a long-lived fluorescent excited state in reactivity.
  • Capturing Variability in Material Property Predictions for Plastics Recycling via Machine Learning
    Marcin Pietrasik, Anna Wilbik, Yannick Damoiseaux, Tessa Derks, Emery Karambiri, Shirley de Koster, Daniel van de Velde, Kim Ragaert, Sin Yong Teng
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-25
     The paper presents a framework utilizing interval-based machine learning to predict material properties in plastics recycling, addressing the challenges of heterogeneous polyolefin blends and stochastic measurements. The study involves a regressor for point estimation and an interval predictor, with empirical evaluations on a real-world dataset showing improved model interpretation and uncertainty prediction in mechanical recycling processes.
  • Circularly polarized luminescence modulation in europium(III) complexes based on camphor-fused bis(pyrazolyl)pyridine ligands
    Narcis Avarvari, Justin Cado, Alexandre Abherv9, Maxime Grasser, Nicolas Vanthuyne, Francesco Zinna, Lorenzo Di Bari, Boris Le Guennic
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-25
     The paper discusses europium(III) complexes formed using chiral camphor-fused bis(pyrazolyl)pyridine ligands. The ligands were fully characterized using single crystal X-ray diffraction. Key findings include strong circularly polarized luminescence (CPL) with glum factors up to ±0.2 and an ability to invert emission polarization between regioisomeric ligands, facilitating CPL modulation.
  • Graph2Mat: Universal graph to matrix conversion for electron density prediction
    Arghya Bhowmik, Pol Febrer, Peter J9rgensen, Miguel Pruneda, Alberto Garcia, Pablo Ordejon
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-25
     The paper introduces Graph2Mat, a method for converting molecular graphs into equivariant matrices to predict electron densities efficiently. It demonstrates state-of-the-art performance using significantly smaller datasets than traditional grid-based methods. Testing on QM9 molecules in DFT calculations yielded a 40% reduction in self-consistent field iterations, enabling new uncertainty measures for practical applications in machine learning-accelerated DFT methodologies.
  • Challenges and Opportunities for Machine Learning Potentials in Transition Path Sampling: Alanine Dipeptide and Azobenzene Studies
    Nikita Fedik, Wei Li, Nicholas Lubbers, Benjamin Nebgen, Sergei Tretiak, Ying Wai Li
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-25
     The paper discusses using machine learning (ML) potentials in transition path sampling (TPS) for the studies of alanine dipeptide and azobenzene. Utilizing the HIP-NN-TS and ANI-1x ML frameworks trained on a 5 million HCNO structure dataset, the study achieved near-quantum accuracy in conformational searches. Key findings include improved accuracy through active learning and challenges in azobenzene isomerization.
  • Data-Driven Many-Body Simulations of Biomolecules with CCSD(T) Accuracy: I Polyalanine in the Gas Phase
    Francesco Paesani, Ruihan Zhou, Ethan F. Bull-Vulpe, Yuanhui Pan
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-25
     The paper presents a data-driven many-body method, MB-nrg, achieving CCSD(T) accuracy for simulating polyalanine in the gas phase. By utilizing modular potential energy functions, the approach reproduces energies and free-energy landscapes more accurately than traditional force fields. It highlights improvements in capturing structural motifs and facilitates flexible sampling in longer peptides, advancing accurate simulations of proteins.
  • Stay Together or Split Up? Investigating Selective Adsorption of Carbon Dioxide and Acetylene in Anion-Pillared Microporous MOFs
    Mikko Karttunen, Yining Huang, Tahereh Azizivahed, Anupom Roy, Lucier Bryan, Shoushun Chen, Chen-Xi Liang
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-25
     The paper investigates selective adsorption of carbon dioxide and acetylene in anion-pillared microporous MOFs, specifically SIFSIX-1-Cu and SIFSIX-3-Cu. Methods include solid-state NMR spectroscopy and density functional theory, revealing SIFSIX-1-Cu favors acetylene while SIFSIX-3-Cu adsorbs both gases. Results enhance understanding of gas separation mechanisms and inform the design of selective adsorption materials.

25 Mar 2025

  • From GPCRs to Kinases: Automating the Path to Universal Pandemic Vaccine Development
    Sumbul , Asif; Debaprasad, Mukherjee
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-24
     The paper discusses automating the development of universal pandemic vaccines by targeting G protein-coupled receptors (GPCRs) and kinases involved in viral entry and replication. It emphasizes the need for such vaccines against varied strains, notably SARS-CoV-2. The authors highlight AI, machine learning, and computational biology as key methods for enhancing vaccine development.
  • Modulation of A3-40 and A4-40 co-assembly by Zinc: getting closer to the biological reality
    Enrico , Falcone; Lucie, de Cremoux; Christelle, Hureau; David, Schmitt; Wojciech, Bal; Ewelina , Stefaniak; Marta D., Wi śniewska; Nicolas, Vitale
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-24
     The paper investigates the effect of Zn(II) ions on the self- and co-assembly of amyloid-b2 peptides A1-40 and A4-40, using pH-dependent X-ray absorption and NMR spectroscopy. It finds that Zn(II) concentration influences assembly behavior, promoting potentially toxic aggregates. A4-16 has a lower Zn affinity than A1-16, highlighting the significance of Zn in Alzheimer’s disease pathology.
  • Complex structure-free compound-protein interaction prediction for mitigating activity cliff-induced discrepancies and integrated bioactivity learning
    Yaowen, Gu; Song, Xia; Qi, Ouyang; Yingkai, Zhang
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-24
     The paper presents CPI2M, a benchmark dataset with about 2 million bioactivity endpoints and activity cliff annotations, addressing challenges in compound-protein interaction (CPI) prediction. The authors introduce GGAP-CPI, a structure-free deep learning model that improves prediction accuracy by mitigating the effects of activity cliffs. GGAP-CPI outperforms several baselines across various scenarios in drug screening.
  • Two-way mechanism of designer biomolecular condensate catalysts
    Ayala, Lampel; Hao, Dong; Tlalit , Massarano; Yuqin , Yang; Avigail, Baruch Leshem; Ori, Eran; Xiaoyu, Wang
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-24
     The paper investigates designer biomolecular condensates formed by histidine peptides that catalyze ester hydrolysis through two mechanisms: Zn2+-dependent coordination and hydrogen bonds in the absence of Zn2+. Utilizing computational and experimental methods, the study highlights the catalytic pathways of these condensates, suggesting their potential in green chemistry and advanced materials.
  • How to generalize machine learning models to both canonical and non-canonical peptides
    Raúl, Fernández-Díaz; Rodrigo, Ochoa; Thanh Lam, Hoang; Vanessa, Lopez; Denis, Shields
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-24
     The paper examines methods for generalizing machine learning models to both canonical and non-canonical peptides. It highlights the superiority of chemical fingerprint-based similarity over traditional metrics in dataset partitioning. Deep-learned embeddings from Chemical Language Models outperform other models, but combining canonical datasets with non-canonical peptides enhances generalization. All supplementary materials are available on GitHub.
  • Machine learning transition state geometries and applications in reaction property prediction
    Isaac W., Beaglehole; Miles J., Pemberton; Elliot H. E., Farrar; Matthew N., Grayson
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-24
     This paper discusses the use of machine learning (ML) for predicting transition state (TS) geometries, aiming to reduce reliance on expensive quantum mechanical calculations. It reviews ML methods, their applications in predicting reaction properties, and the importance of accurate TS data. The paper also evaluates the limitations and challenges of current TS prediction methods.

22 Mar 2025

  • A 3D-Bioprinted Dermal-Like Scaffold Incorporating Fibroblasts and DRG Neurons to investigate Peripheral Nerve Regeneration
    Simone Bonetti, Francesco Formaggio, Emanuela Saracino, Eva Clemente, Marianna Barbalinardo, Franco Corticelli, Sara Buoso
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-21
     The paper presents a 3D-bioprinted fibroblast/DRG neuron co-culture scaffold to study peripheral nerve regeneration. The study optimized bioprinting parameters, achieving a biocompatible construct that supports cell growth. Results showed high viability of fibroblasts and enhanced neurite outgrowth in DRG neurons, emphasizing fibroblasts’ role in axonal regeneration, and offering a platform for neuropathic pain therapies.

  • AlphaFold2-RAVE: Protein Ensemble Generation with Physics-Based Sampling
    Da Teng, Vanessa J. Meraz, Akashnathan Aranganathan, Xinyu Gu, Pratyush Tiwary
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-21
     The paper presents AlphaFold2-RAVE (af2rave), an open-source Python tool that combines machine learning and physics-based sampling to generate alternative protein conformations. It utilizes reduced MSA from AlphaFold2 and molecular dynamics simulations, validating on E. coli adenosine kinase and human DDR1 kinase. af2rave offers efficient sampling, outperforming traditional methods, making it a valuable resource for structural biology and drug discovery.

  • Detecting apelin isoforms in human plasma using LC-MS/MS
    Maxwell Zeigler, Rozenn N. Lemaitre, Nona Sotoodehnia, Rheem Totah
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-21
     The paper presents a novel ultra-high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for quantifying apelin isoforms (apelin-12, apelin-13, [Pyr1]-apelin-13, and apelin-17) in human plasma. This method achieves picomolar sensitivity and less than 20% variation, enabling the detection of apelin peptides as potential biomarkers for various health conditions.

  • SubTuner: a Physics-Guided Computational Tool for Modifying Enzymatic Substrate Preference and Its Application to Anion Methyltransferases
    Qianzhen Shao, Asher C. Hollenbeak, Yaoyukun Jiang, Brian O. Bachmann, Zhongyue J. Yang, Xinchun Ran
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-21
     The paper presents SubTuner, a physics-guided computational tool for modifying enzymatic substrate preference, particularly for anion methyltransferases. It utilizes three datasets with 190 and 600 mutants to demonstrate its speed, accuracy, and predictivity in identifying beneficial enzyme mutations for non-native substrates. SubTuner outperformed other engineering tools, highlighting its potential for enzyme engineering applications.

  • Hierarchical incremental learning deciphers multi-component materials
    Haoyuan Li, Hanyin Zhang, Nan Lin, Austin Evans, Tonghui Wang, Saied Pratik, Jean-Luc Bredas
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-21
     The paper presents HiDiscover, a hierarchical incremental learning protocol for analyzing multi-component materials. It enhances the understanding of Li-ion transport, gas adsorption, and molecular packing by revealing microscopic features not easily detectable with traditional simulations. This method improves reliability across different materials and processes, showcasing the effectiveness of incremental learning in materials design.

  • Active learning meets metadynamics: Automated workflow for reactive machine learning potentials
    Fernanda Duarte, Veronika Juraskova, Tristan Johnston-Wood, Hanwen Zhang, Valdas Vitartas
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-21
     The paper presents an automated workflow that integrates active learning and well-tempered metadynamics to enhance machine learning-based potentials (MLPs) for reaction modeling. It utilizes data-efficient architectures and demonstrates effectiveness through various organic reactions, achieving accurate MLPs without prior transition state knowledge, thus expanding applicability in reactive systems.

  • Mapping proton-coupled electron transfer with real space dimensions
    Adam Srut, Martin Diefenbach, Marvin L. Kronenberger, Benjamin J. Lear, Vera Krewald
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-21
     The paper presents a computational method for mapping proton-coupled electron transfer (PCET) onto real space dimensions, generating potential energy surfaces. It introduces a new criterion for ‘concerted’ proton-electron transfer by identifying proton and electron transfer coordinates, enhancing the understanding of their contributions and distinguishing between concerted and coupled transfer processes.

  • Quantifying Multidimensional Effects of Physicochemical Parameters on PFAS Adsorption Using a Hybrid Response Surface Methodology-Machine Learning Approach
    Renzun Zhao, Harsh V. Patel, Jazmin Green, Hyo-Shin (John) Park, Stephanie Luster-Teasley Pass
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-21
     The study develops hybrid response surface methodology-machine learning models to optimize PFAS adsorption. It utilizes a comprehensive dataset of experimental adsorption data, incorporating parameters like pH and molecular properties. The meta-learning HOP-RSM-GB model achieved near-perfect accuracy (R² = 1.00). Findings reveal optimal conditions for adsorption and emphasize the benefits of combining statistical and machine learning techniques in environmental remediation.

  • Toward Predictive Data-Driven Atomistic Modeling of Electrocatalyst Stability and Surface Reconstruction
    Jiayu Peng
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-21
     The paper evaluates methods for modeling electrocatalyst stability and surface reconstruction, addressing challenges in understanding dissolution and restructuring. It examines classical and data-driven approaches, including first-principles simulations and machine learning techniques, highlighting their strengths and weaknesses. The findings aim to enhance predictive modeling for optimizing electrocatalysts under harsh conditions.

  • CoRE MOF DB: a curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening
    Yongchul Chung, Guobin Zhao, Logan Brabson, Saumil Chheda, Ju Huang, Thang Pham, Lionel Zoubritzky, Kunhuan Liu, Gianmarco Terrones, Kenji Mochida, Sunghyun Yoon, Haewon Kim, Prerna Prerna, Mohamad Moosavi, François-Xavier Coudert, Heather Kulik, David Sholl, Ilja Siepmann, Randall Snurr, Maciej Haranczyk
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-21
     The paper introduces an updated CoRE MOF database, featuring a curated collection of computation-ready metal-organic frameworks (MOFs) for efficient materials discovery. It includes machine-learned properties and employs Gibbs-Ensemble Monte Carlo simulations for hydrophobicity classification. The dataset was used for integrated material-process screening, identifying several MOF candidates that may exceed the performance of CALF-20 in carbon capture scenarios.

  • Machine learning reveals structural characteristics of stereochemistry-specific interdigitation of synthetic monomycoloyl glycerol analogs
    Suvi Heinonen, Artturi Koivuniemi, Matthew Davies, Mikko Karttunen, Camilla Foged, Alex Bunker
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-21
     The paper investigates the structural characteristics of synthetic monomycoloyl glycerol (MMG) analogs using machine learning (ML) and molecular dynamics (MD) simulations. It compares two analogs, MMG-1 and MMG-6, analyzing their phase behavior across three membrane states. Results indicate that interdigitation propensity correlates with hydrocarbon chain ordering, enhancing understanding of lipid-based formulations.

  • Quantifying design principles for light-emitting materials with inverted singlet-triplet energy gaps
    Laura McCaslin, Varun Rishi, Ali Abou Taka, Hrant Hratchian
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-21
     The paper analyzes design principles for organic light-emitting materials with inverted singlet-triplet energy gaps (ΔEST). Using a benchmark dataset of 15 heptazine-based molecules (HEPTA-INVEST15) and an expanded set (HEPTA-INVEST46), it finds strong linear correlations (R² > 0.94) between molecular structure and ΔEST. The study emphasizes the importance of strategic functionalization for efficient design of high-performance organic emitters.

  • A Minimalistic Deep Graph Learning Approach for Protein-Ligand Binding Affinity: One Step Towards Generalization
    Norberto Sánchez-Cruz, Ulises Rojas-Castañeda, Gabriel A. Argüelles-Arjona, Víctor H. Ramón-Cetina
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-21
     The paper presents ECIF-GCN, a minimalist deep graph learning model for predicting protein-ligand binding affinity, trained on the LP-PDBbind dataset. Utilizing Graph Convolutional Networks, it achieved a test RMSE of 1.52, outperforming more complex models. This demonstrates that effective prediction can be attained without overparameterization, emphasizing the benefits of simpler architectures in molecular modeling.

21 Mar 2025

  • Domain adaptable language modeling of chemical compounds identifies potent pathoblockers for Pseudomonas aeruginosa
    Georgios, Kallergis; Ehsannedin, Asgari; Martin, Empting; Anna, Hirsch; Alice, McHardy; Frank, Klawonn
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-20
     The paper presents ChemLM, a transformer-based language model for predicting molecular properties and identifying potent pathoblockers for Pseudomonas aeruginosa. It utilizes self-supervised domain adaptation, achieving superior performance on benchmark datasets. ChemLM showed significantly higher accuracy in identifying pathoblockers compared to existing methods, confirming its effectiveness and applicability in chemical property prediction.

  • Tuning Antiaromaticity through meso-Substituent Orientation in Core-Modified Isophlorins
    Ken-ichi, Yamashita; Maika, Isoda; Haruna, Sugimura; Yusuke, Honda
    Organic Chemistry on ChemRxiv
    2025-03-20
     This study explores the impact of meso-substituents on the antiaromaticity of stable dithiadioxaisophlorins (S2O2Iphs). It synthesized two new derivatives and utilized techniques like ¹H NMR and UV/vis spectroscopy alongside computational methods. Results reveal that steric bulk influences the tilt angle of substituents, affecting antiaromaticity, which aids in designing materials with adjustable electronic properties.

  • Bulky Bipyridine-Type Ligand-Enabled para-Selective C–H Borylation of Aromatic Compounds
    Yoichiro, Kuninobu; Taisei, Enta; Genki, Yoshino
    Organic Chemistry on ChemRxiv
    2025-03-20
     The paper presents a method for para-selective C–H borylation of aromatic compounds using iridium catalysts with bulky bipyridine-type ligands. By incorporating bulky substituents at specific positions, the method enhances para-selectivity, even with small substituents. Results indicate improved selectivity over traditional methods.

  • Electrochemical Oxidation of Benzyl Alcohols via Hydrogen Atom Transfer Mediated by 2,2,2-Trifluoroethanol
    Takahiro, Kawajiri; Masahiro, Hosoya; Satoshi, Goda; Eisuke, Sato; Seiji, Suga
    Organic Chemistry on ChemRxiv
    2025-03-20
     The paper presents a novel electrochemical oxidation method for benzyl alcohols using 2,2,2-trifluoroethanol as a hydrogen atom transfer mediator. Density-functional theory, cyclic voltammetry, and constant potential electrolysis validate the HAT mechanism, enabling oxidation of challenging substrates. The resulting carbonyl compounds can be further functionalized in an electrochemical one-pot process, enhancing their synthetic utility.

  • Direct Site-selective Deoxygenation of Benzylalcohol Derivatives
    Yoshihiro, Sato; Satoshi, Yagi; Kazuhiro, Morisaki
    Organic Chemistry on ChemRxiv
    2025-03-20
     The paper describes a straightforward method for the direct and site-selective deoxygenation of benzyl alcohol derivatives using Hantzsch ester, Cs₂CO₃, and a photocatalyst under photoirradiation. Mechanistic studies indicate that in situ reversible acylative activation of hydroxy groups is crucial for selectivity, providing a practical strategy for transforming polyols.

  • Structure-Based Modeling of Environment-dependent Protonation States Across LNP Formulations with Atomistic CpHMD
    Severin T., Schneebeli; Kyle J., Colston; Santiago C., Monsalve
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-20
     The paper introduces a scalable continuous constant pH molecular dynamics (CpHMD) model to study environment-dependent protonation states in lipid nanoparticle (LNP) formulations. It simulated five LNP formulations, yielding mean apparent pKa values with a 0.5 unit error compared to experiments. This method enhances understanding of ionizable lipids’ charge distributions, impacting drug delivery and manufacturing.

  • Generative Design of Singlet Fission Materials by Revisiting the Use of a Fragment-oriented Database
    Thanapat, Worakul; Rubén, Laplaza; J. Terence, Blaskovits; Clémence, Corminboeuf
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-20
     The paper utilizes the FORMED repository of 116,687 synthesizeable molecules to conduct fragment-oriented virtual screening and genetic algorithms for identifying singlet fission materials. By employing reinforcement learning, the authors rediscover various known chromophores and propose a novel compound, neocoumarin, displaying diradicaloid behavior. The results emphasize the efficacy of generative design in expanding material discovery.

  • Quantifying design principles for light-emitting materials with inverted singlet-triplet energy gaps
    Laura, McCaslin; Varun, Rishi; Ali, Abou Taka; Hrant, Hratchian
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-20
     The paper explores design principles for light-emitting materials with inverted singlet-triplet gaps (ΔEST). Using the HEPTA-INVEST15 dataset of 15 heptazine-based molecules, it finds a strong correlation (R2 > 0.94) between intramolecular charge transfer and ΔEST. Key findings highlight the impact of functional groups on ΔEST, offering predictive metrics for future organic emitter design using machine learning methods.

  • Dual-sites for low-concentration NO to NH3 electrosynthesis in neutral media: promoting NO adsorption and water dissociation
    Tongwei, Wu; Xiaoxi, Guo; Hengfeng , Li; Yanning , Zhang; Chao, Ma; Hongmei , Li; Haitao, Zhao; Liyuan, Chai; Min, Liu
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-20
     The paper presents a novel dual-site catalyst combining Pt nanoparticles (PtNPs) and an Fe single-atom catalyst (FeSAC) to enhance electrosynthesis of ammonia (NH3) from low-concentration NO in neutral media. Methods include in situ X-ray absorption and infrared spectroscopy. Results show a Faradaic efficiency of 90.3% and a record NH3 yield of 3023.8 µg h-1 mgcat.-1, surpassing traditional catalysts.

20 Mar 2025

  • Molecularly defined glycocalyx models reveal AB5 toxins recognize their target glycans superselectively
    W. Bruce Turnbull, Laia Saltor Nf1ez, Vajinder Kumar, James F. Ross, Jonathan P. Dolan, Sumitra Srimasorn, Xiaoli Zhang, Ralf P. Richter
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-19
     The paper discusses the development of glycocalyx models utilizing mucin-like glycopolymers to study AB5 toxins like cholera and shiga toxins. Methods employed include quartz crystal microbalance and spectroscopic ellipsometry to analyze toxin binding kinetics. Results indicate that toxins show significantly enhanced affinity in these models, suggesting superselective binding critical for host cell selection.
  • Evolutionary Machine Learning of Physics-Based Force Fields in High-Dimensional Parameter-Space
    David van der Spoel, Julian Marrades, Kristian Kriz, A. Najla Hosseini, Alfred Nordman, Joao Paulo Ateide Martins, Paul J. van Maaren, Mohammad Mehdi Ghahremanpour, Marie-Madeleine Walz
    Organic Chemistry on ChemRxiv
    2025-03-19
     The paper introduces the Alexandria Chemistry Toolkit (ACT), an open-source software for machine learning physics-based force fields (FFs) using customizable potential functions. It employs a genetic algorithm and Monte-Carlo methods to optimize FF parameters. The evaluation of different models demonstrates improved prediction accuracy for organic molecules’ properties in gas and liquid phases.
  • MASSISTANT: A Deep Learning Model for De Novo Molecular Structure Prediction from EI-MS Spectra via SELFIES Encoding
    Marcin Pietrasik, John Mommers, Lazar Barta, Anna Wilbik
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-19
     The paper presents MASSISTANT, a deep learning model for predicting molecular structures from low-resolution EI-MS spectra using SELFIES encoding. Using the NIST dataset with 180k spectra, MASSISTANT achieves about 10% exact predictions, improving to 54% with a curated dataset. The model enhances the interpretation of chromatographic peaks in mass spectrometry, leveraging complex fragmentation patterns to generate valid chemical structures.
  • Data-driven discovery of water-stable metal-organic frameworks with high water uptake capacity
    Akash Ball, Gianmarco Terrones, Shuwen Yue, Heather Kulik
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-19
     The paper presents a machine learning and high-throughput screening approach to discover water-stable metal-organic frameworks (MOFs) with high water uptake capacity. Utilizing 736 curated MOFs and grand canonical Monte Carlo simulations, the study identifies strong correlations between pore features and water uptake. Ultimately, 74 promising MOFs are predicted to exhibit both water stability and high capacity.
  • 3D2SMILES: Translating Physical Molecular Models into Digital DeepSMILES Notations Using Deep Learning
    Wenqi Guo, Yiyang Du, Mohamed Shehata
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-19
     The paper presents 3D2SMILES, a deep learning model that converts images of physical molecular models into digital DeepSMILES notations. Utilizing both synthetic and real datasets, it achieved 62.0% top-1 and 80.3% top-3 accuracy. The study also addresses model explainability, limitations, and future research directions.

19 Mar 2025

  • Data Scaling and Generalization Insights for Medicinal Chemistry Deep Learning Models
    Alan Cheng, Jacky Chen, Song Yang, Jonathan Tynan, Cheng Chen, Yunsie Chung
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-18
     The paper investigates deep learning models for drug discovery, comparing them to traditional methods like XGBoost and random forest using internal and publicly available datasets. It examines performance across various data tasks and identifies factors boosting graph neural network performance. A scaling relationship accounting for 81% of performance variance is developed, aiding in estimating models for ADMET endpoints and enhancing drug discovery outcomes.
  • Machine Learning-Guided Synthesis of Prospective Organic Molecular Materials: An Algorithm with Latent Variables for Understanding and Predicting Experimentally Unobservable Reactions
    Norie MOMIYAMA, Kazuhiro TAKEDA, Naoya OHTSUKA, Toshiyasu SUZUKI
    Organic Chemistry on ChemRxiv
    2025-03-18
     The paper presents a machine learning algorithm that incorporates latent variables to predict unobservable reactions for perfluoro-iodinated naphthalene derivatives. The algorithm achieved an R2 value >0.99, accurately estimating substitution patterns and reaction yields, validated experimentally. This ML-guided framework enhances heuristic methods in chemistry, optimizing synthetic processes and enabling further applications in catalyst discovery and organic semiconductor optimization.
  • Transferable Machine Learning Interatomic Potential for Pd-Catalyzed Cross-Coupling Reactions
    Olexandr Isayev, Dylan Anstine, Roman Zubatyuk, Liliana Gallegos, Robert Paton, Olaf Wiest, Benjamin Nebgen, Travis Jones, Gabe Gomes, Sergei Tretiak
    Organic Chemistry on ChemRxiv
    2025-03-18
     The paper presents AIMNet2-Pd, a machine learning interatomic potential for palladium-catalyzed cross-coupling reactions, enabling rapid computational studies. Trained on monophosphine ligands, it performs energy calculations and geometry optimizations with high accuracy (1-2 kcal mol−1 and ~0.1 Å) in seconds, facilitating high-throughput catalyst screening and mechanistic studies of diverse Pd complexes.
  • Ascorbate Peroxidase (APEX2) Activates Dihydrotetrazine Oxidation for Rapid Bioorthogonal Chemistry in Living Cells
    Joseph Fox, Sophia Neglia, Amanda Tallon, Christopher am Ende, Colin Thorpe
    Organic Chemistry on ChemRxiv
    2025-03-18
     The paper demonstrates that APEX2 efficiently catalyzes dihydrotetrazine (DHTz) oxidation, enhancing bioorthogonal reactions in living cells. Through in vitro studies, it reveals the role of superoxide dismutase and the impact of H2O2 on reaction efficiency (kcat/KM 4.90 × 10^3 M−1s−1). Tests included live HeLa and PC3 cells, showing successful tetrazine conjugation with fluorescent tags and selective targeting of enzymes.
  • Addressing sustainability challenges in peptide synthesis with flow chemistry and machine learning
    Kristóf Ferentzi, András Perczel, Viktor Farkas
    Organic Chemistry on ChemRxiv
    2025-03-18
     The paper presents a recyclable Anisole/DMSO solvent system for flow chemistry in peptide synthesis, outperforming DMF. Key methods included solvent parameter exploration, flow parameter adjustments reducing racemization, and machine learning optimization. High-efficiency synthesis of peptides (Aib-ACP, GLP-1, BPTI) was achieved, making the process more sustainable by minimizing toxic waste and improving speed.
  • Racemization-free peptide bond formation via 2-nitrobenzensulfonyl strategy for diastereoselective synthesis of (Z)-fluoroalkene-type peptidomimetcs
    Tetsuo NARUMI, Chihiro Iio, Kohei Sato, Nobuyuki Mase
    Organic Chemistry on ChemRxiv
    2025-03-18
     The paper discusses a novel racemization-free strategy for synthesizing (Z)-fluoroalkene-type peptidomimetics, using the 2-nitrobenzensulfonyl (Ns) group to prevent racemization during peptide bond formation. The method involves coupling Xaa-Pro-type FADIs with amino acid benzyl esters, enhancing diastereoselectivity and expanding the applications of FADIs in peptide development.
  • Chiral Potassium Brønsted Base-Catalyzed Stereoselective Synthesis of 1,3-Diols via a Tandem Allylic Isomerization/Asymmetric Aldol–Tishchenko Reaction
    Masahiro Sai, Hiroki Ishikawa
    Organic Chemistry on ChemRxiv
    2025-03-18
     The paper discusses chiral potassium Brønsted bases featuring 3,3́-substituted BINOL-based crown ethers as sustainable catalysts for a tandem allylic isomerization/asymmetric aldol–Tishchenko reaction. This method produces diverse 1,3-diols with excellent diastereoselectivity and high enantioselectivity, utilizing allylic alcohols as nucleophiles.
  • An Electrochemical Amidation/C-H Halogenation Cascade for the Sustainable Synthesis of Halogenated N-Aryl Amides, Carbamates and Ureas
    Oscar Verho, Sudipta Ponra, Ruzal Sitdikov, Alyssio Calis
    Organic Chemistry on ChemRxiv
    2025-03-18
     The paper presents an electrochemical cascade method for synthesizing halogenated N-aryl amides, carbamates, and ureas, combining amide bond formation with C-H chlorination. Demonstrating its robustness, over 180 products were synthesized, highlighting its scalability and environmental benefits compared to traditional methods.

18 Mar 2025

  • Molecular deep learning at the edge of chemical space
    Derek van Tilborg, Luke Rossen, Francesca Grisoni
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-17
     The paper proposes a joint modeling approach to enhance molecular machine learning’s ability to generalize beyond training data. Utilizing over 30 bioactivity datasets, it introduces a reconstruction-based “unfamiliarity” metric to predict model performance on novel bioactive molecules. The results show that this method effectively identifies out-of-distribution molecules and offers deeper molecular insights compared to traditional approaches.
  • How repair proteins identify DNA damage in the nucleosome
    Fabrizio Cleri, Parvathy A. P. Sarma, Safwen Ghediri, Vinnarasi Saravanan, Corinne Abbadie, Ralf Blossey
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-17
     The paper investigates how repair proteins identify DNA damage in nucleosomes using molecular dynamics simulations. It focuses on interactions between glycosylase UDG and mutated uracil, and PARP1 with simulated single-strand breaks. Results suggest that specific mechanical deformations around DNA defects may enhance damage recognition, making random search insufficient for efficient repair initiation.
  • Neural Mulliken Analysis: Molecular Graphs from Density Matrices for QSPR on Raw Quantum-Chemical Data
    Oleg Gromov
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-17
     The paper introduces Neural Mulliken Analysis, leveraging molecular graphs from one-electron density matrices for QSPR. Utilizing atomic and link node embeddings, the method employs a GNN trained on data from the Solubility Challenge, achieving improved solubility predictions (RMSE 0.63, R^2 0.79). This approach shows potential for enhancing chemical machine-learning tasks by integrating electronic structure awareness.
  • Leveraging Alchemical Free Energy Calculations with Accurate Protein Structure Prediction
    Jan Domański, Jenke Scheen, Francesco Rianjongdee, Hannah Bruce Macdonald, Richard Gowers, Seb Degorce, Adam Green, Conor Scully, Tom Duffy, Jen Howes, Charlotte Cordery, Alwin Bucher, Laksh Aithani
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-17
     The paper presents a hybrid framework combining machine-learned ligand-protein co-folding models with Free Energy Perturbation (FEP) for drug discovery. It benchmarks results on a public kinase target (PFKFB3) and an internal target, showing improved accuracy and reduced computational costs compared to traditional methods, thus enhancing lead optimization and supporting therapeutic discovery.
  • Time-Series Learning Based on Neural Ordinary Differential Equations for Nonadiabatic Molecular Dynamics Simulations
    Haoyang Xu, Jin Wen, Luxiang Zhu, Lei Niu, Feng Yan, Meifang Zhu
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-17
     The paper presents two time-series learning models based on neural ordinary differential equations (NODEs) for nonadiabatic molecular dynamics simulations: continuous normalizing flow (CNF) and Hamiltonian neural networks (HNN). CNF improves modeling of energy gap distributions, while HNN effectively handles nonadiabatic transitions. Both architectures show potential for ultrafast dynamics simulations, with CNF simplifying parameter tuning and HNN enhancing configurational reorganization simulations.

14 Mar 2025

  • mineMS2: Annotation of spectral libraries with exact fragmentation patterns
    Etienne A., Thévenot, Alexis, Delabrière, Coline, Gianfrotta, Sylvain, Dechaumet, Pierrick, Roger, Annelaure, Damont, Thaïs, Hautbergue, Emilien L., Jamin, Olivier, Puel, Christophe, Junot, François, Fenaille
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-13
     The paper presents mineMS2, a software for annotating spectral libraries using exact fragmentation patterns. It employs a novel graph-based representation and a frequent-subgraph mining algorithm. Evaluated on spectral databases and biological samples, mineMS2 identifies similarities overlooked by traditional methods, enhancing structural interpretation and the identification of unknown metabolites. The software is available as an R package.

  • HyperXtract: Strategic Platform for Optimizing High-Bandwidth Nanopore Data Extraction Performance
    Y.M. Nuwan, D.Y. Bandara, Katherine, Gussenhoven, Dhanush, Amarasekara, Jugal, Saharia
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-13
     The paper introduces HyperXtract (HX), a platform optimizing high-bandwidth nanopore data extraction, outperforming its predecessor, NanoPlex (NP). HX processes 1000-second files at 40 MHz in under 50 seconds, significantly faster than NP’s >45× slower performance. Key methods include MEX functions, memory mapping, and pre-allocation for efficient analysis, allowing near real-time decision-making in high-bandwidth experiments.

  • Graph neural networks to predict atomic transition charges and exciton couplings in organic semiconductors
    Daniel, Packwood, Geoffrey, Weal, Maryam, Nurhuda, Justin, Hodgkiss, Paul, Hume
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-13
     The paper presents a graph neural network (GNN) model that predicts exciton couplings in organic semiconductors using atomic transition charges. It demonstrates successful predictions for fused-ring electron acceptors and other molecules from the Cambridge Crystallographic Data Centre, enabling accurate exciton diffusion simulations and advancing high-throughput virtual screening for photovoltaic applications.

  • Comparison of Magnesium and Manganese Ions on the Structural and Catalytic Properties of Human DNA Polymerase Gamma
    Arkanil, Roy, G. Andrés, Cisneros
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-13
     The paper compares the effects of magnesium (Mg²⁺) and manganese (Mn²⁺) on human DNA polymerase gamma (Pol γ) using molecular dynamics and quantum mechanics/molecular mechanics simulations. Mn²⁺ enhances catalytic efficiency and flexibility but reduces structural stability compared to Mg²⁺. Findings indicate a trade-off between these properties, with implications for mutagenesis and mitochondrial disorders.

  • Cooperative Free Energy: Induced Protein–Protein Interactions and Cooperative Solvation in Ternary Complexes
    Sereina, Riniker, Shu-Yu, Chen, Riccardo, Solazzo, Marianne, Fouché, Hans-Jörg, Roth, Birger, Dittrich
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-13
     The paper investigates cooperative free energy in ternary complexes, emphasizing induced protein-protein interactions (PPIs) and cooperative solvation. Using an approximated expression, it developed a rapid computational method to predict cooperativity in eight complexes, achieving a Kendall τ of 0.79. The study critiques current interpretations of cooperativity and provides thermodynamic insights into protein-ligand-protein interactions.

13 Mar 2025

  • Conformal Selection for Efficient and Accurate Compound Screening in Drug Discovery
    Tian, Bai; Peng, Tang; Yuting, Xu; Vladimir, Svetnik; Abbas, Khalili; Xiang, Yu; Archer, Yang; Bingjia, Yang
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-12
     The paper presents a conformal selection approach for compound screening in drug discovery, addressing bias and risk control issues. By using conformal inference to generate p-values, it ensures rigorous false discovery rate control and high accuracy. The method shows improved computational efficiency and is validated through numerical simulations on real-world datasets.
  • DeepDeg: Forecasting and explaining degradation in novel photovoltaics
    Felipe, Oviedo; David S., Hayden; Thomas, Heumeuller; Jonas, Wortmann; Jose Dario, Perea; Richa, Naik; Hansong, Xue; John, Fisher III; Christoph J, Brabec; Tonio, Buonassisi; Juan, Lavista
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-12
     The paper presents DeepDeg, a machine learning model that forecasts and explains degradation in novel photovoltaics. Using a dataset of over 785 stability tests (230,000 hours), it accurately predicts degradation dynamics and explains the factors involved. Evaluated with 9,000 hours of data, DeepDeg accelerates degradation characterization by 5-20 times.
  • Automated Predictive Chemical Reaction Modelling applied to Gold(I) Catalysis
    Raphaël, Robidas; Claude Y., Legault
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-12
     The paper presents a theoretical framework for automated predictive chemical reaction modeling applied to gold(I) catalysis, utilizing a “neophile” kinetic model. It achieved successful modeling of 17 unimolecular reactions, closely matching experimental results in 11 cases. This approach aims to facilitate unbiased reaction data generation and future machine-learning applications for autonomous research.
  • Exploring Chemistry and Catalysis by Biasing Skewed Distributions via Deep Learning
    Zhikun, Zhang; GiovanniMaria, Piccini
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-12
     The paper presents Loxodynamics, a deep learning approach for automating chemical reaction discovery using biased molecular dynamics. It utilizes a skewed distribution to identify low-energy reaction pathways, employing Skewencoder for efficient data extraction. Validated on various reactions, including gas-phase and catalytic processes, Loxodynamics provides a novel, data-driven framework for navigating complex chemical systems.
  • Harnessing Electric Fields for Rare Earth Element Recovery: A Computational Study of Electromigration and Flow Dynamics Using Dilute Feedstock
    Venkateshkumar, Prabhakaran; Zirui, Mao; Zhijie, Xu; Yang, Huang; Giovanna, Ricchiuti; Bruce, Palmer; Pauline G. , Simonnin; Vijayakumar, Murugesan; Grant E, Johnson; Jaehun , Chun
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-12
     The paper presents a Computational Fluid Dynamics (CFD) study exploring electrohydrodynamic methods for separating rare earth elements (REEs) from dilute feedstocks. It identifies key separation regimes influenced by diffusion, advection, and electromigration. Optimal conditions for separation require slow flows and strong electric fields. Results emphasize the significance of channel design and flow dynamics for effective REE recovery.
  • Comparing Massively-Multitask Regression Algorithms for Drug Discovery
    Eric, Martin; Xiang-Wei, Zhu; Patrick, Riley; Steven, Kearnes; Ekaterina A , Sosnina; Li, Tian; Ying, Wei; Thomas M, Whitehead; Gareth J, Conduit; Matthew D, Segall
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-12
     The paper compares six Massively-Multitask Regression Models (MMRMs) for bioactivity prediction in drug discovery, using datasets of 159 kinase and 4276 diverse ChEMBL assays. Results show MMRMs outperform single-task random forests, particularly in hit-finding, but not for virtual screening. Training on 99+% of data yields better accuracy than training on 75%. Practical recommendations for method selection are discussed.

12 Mar 2025

  • Validation of dried saliva for molecular diagnostics
    Charles R., Mace; Andrea C., Mora; Alexandra K., Sogn; Allison J., Tierney; Elizabeth, Tzavaras; Mabi L., Singh; Gustavo, Mahn Arteaga; Fiorenzo G., Omenetto; Athena, Papas
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-11
     The paper discusses the validation of FishburneTabs for standardized saliva sampling in viral diagnostics, particularly for COVID-19. It involves a method where patients collect saliva on paper tabs that dry and can be processed like dried blood spots. With 125 clinical samples, this method achieved 85% sensitivity and 94% specificity, offering a reliable alternative to liquid saliva for decentralized testing.

  • A one-pot RT-LAMP diagnostic assay for SARS-CoV-2 from saliva samples
    Charles R., Mace; Andrea C., Mora; Allison J., Tierney; Alexandra K., Sogn; Paul T., Lawrence; Elizabeth, Tzavaras; Mabi L., Singh; Gustavo, Mahn Arteaga; Fiorenzo G., Omenetto; Athena, Papas
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-11
     The paper describes a one-pot RT-LAMP assay for detecting SARS-CoV-2 in saliva, addressing challenges of saliva’s complexity. The method involves five steps, including heating and reaction processes. Tested on 127 patient samples, the assay achieved 98% accuracy, 88% sensitivity, and 100% specificity, demonstrating potential for improved point-of-care diagnostics for respiratory viruses.

  • Novel Tacrine-Based Compounds: Bridging Cholinesterase Inhibition and NMDAR Antagonism
    Jan, Korabecny; Barbora, Svobodova; Zuzana, Moravcova; Anna, Misiachna; Gabriela, Novakova; Ales, Marek; Vladimir, Finger; Jitka, Odvarkova; Jaroslav, Pejchal; Jana, Karasova Zdarova; Jakub, Netolicky; Marek, Ladislav; Martina, Hrabinova; Ales, Sorf; Lubica, Muckova; Lenka, Fikejzlova; Marketa, Benkova; Martin, Novak; Lukas, Prchal; Jan, Capek; Jiri, Handl; Tomas, Rousar; Katarzyna, Ewa Greber; Krzesimir, Ciura; Martin, Horak; Ondrej, Soukup
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-11
     The paper discusses the design and evaluation of 16 novel tacrine derivatives for Alzheimer’s disease treatment, focusing on combining cholinesterase inhibition and NMDAR antagonism. In silico screening and in vitro assays assessed bioavailability and safety, revealing compound 5m as a potent inhibitor with improved metabolic stability and CNS penetration. The study supports further development of these multi-target ligands.

  • DNA-programmable Protein Degradation: Dynamic Control of PROTAC Activity via DNA Hybridization and Strand Displacement
    Michael, Booth; Disha, Kashyap; Thomas, Milne
    Organic Chemistry on ChemRxiv
    2025-03-11
     The paper presents OligoPROTACs, DNA-linked PROTACs that enable controlled protein degradation through DNA hybridization and a dynamic off-switch mechanism via strand displacement. The study showcases how these constructs can achieve distance-dependent degradation and reduce off-target effects, suggesting DNA nanotechnology’s potential to refine PROTAC systems for safer therapeutic applications.

  • Enzymatic Crosslinking of Polyelectrolyte Complexes Produces Strong, Reconstitutable Biomedical Adhesives
    Aasheesh, Srivastava; Tanmay , Dutta; Bhanwar, Lal; Drishya, Vats; Roop Singh, Lodhi; Paramita, Das; Debasis, Nayak
    Biological and Medicinal Chemistry on ChemRxiv
    2025-03-11
     The paper presents a novel water-based biomedical adhesive formed from anionic and cationic polyelectrolytes with Horseradish Peroxidase. It demonstrated strong adhesion (up to 10 MPa) on various surfaces and superior hemostatic properties in vivo compared to commercial adhesives. The adhesive is also reconstitutable from a lyophilized form, indicating potential for effective wound healing applications.

11 Mar 2025

  • Machine Learning-Based Retention Time Prediction Tool for Routine LC-MS Data Analysis
    Oleksandr O., Grygorenko; Sofiia A., Dymura; Oleksandr O., Viniichuk; Kostiantyn P., Melnykov; Dmytro S., Radchenko
    Organic Chemistry on ChemRxiv
    2025-03-10
     The paper presents a machine learning model for predicting retention time (RT) in LC-MS data analysis using a GATv2Conv+DL graph neural network. Trained on internal data and evaluated with the METLIN SMRT dataset, the model achieved a mean absolute error of 2.83 seconds. It was integrated into an existing LC-MS tool, with 95% of data within the RT range of -11.34 to +10.68 seconds.

  • Investigating Cavity Quantum Electrodynamics-Enabled Endo/Exo- Selectivities in a Diels-Alder Reaction
    Pengfei, Huo; Jialong, Wang; Braden, Weight
    https://chemrxiv.org/engage/rss/chemrxiv?categoryId=605c72ef153207001f6470ce
    2025-03-10
     The paper explores how cavity quantum electrodynamics can alter selectivity in a Diels-Alder reaction, using a parameterized quantum electrodynamic (pQED) approach. It shows that coupling to the cavity changes selectivity from equal probabilities of Endo/Exo isomers outside the cavity. The findings align with high-level QED methods, demonstrating electron density redistribution due to the cavity effects.

  • When Theory Came First: A Review of Theoretical Chemical Predictions Ahead of Experiments
    Mario, Barbatti
    https://chemrxiv.org/engage/rss/chemrxiv?categoryId=605c72ef153207001f6470ce
    2025-03-10
     The paper reviews twenty examples over fifteen years where theoretical predictions in computational chemistry anticipated experimental findings in fields like bioinorganic chemistry and materials science. It highlights the essential role of quantum chemical methods in predicting molecular structures, reaction mechanisms, and material properties, demonstrating their impact on guiding experiments and scientific discovery.

  • On the Reproducibility of Free Energy Surfaces in Machine-Learned Collective Variable Spaces
    Matteo, Salvalaglio; Florian M., Dietrich
    https://chemrxiv.org/engage/rss/chemrxiv?categoryId=605c72ef153207001f6470ce
    2025-03-10
     The paper discusses reproducibility in machine-learned collective variable (MLCV) spaces for calculating free energy surfaces (FES). It identifies the influence of training variability and hyperparameter roughness on reproducibility, proposing a Geometric Free Energy representation and a normalization factor to unify definitions and improve gradient modeling. These approaches aim to enhance the adoption of MLCVs in molecular simulations.

07 Mar 2025

  • Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data
    Johannes, Kirchmair; Hosein, Fooladi; Thi Ngoc Lan, Vu
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-06
     The paper evaluates twelve machine learning models, including random forests and graph neural networks, on eight datasets to assess their robustness on out-of-distribution (OOD) data. It reveals that OOD data, particularly chemical similarity clustering, poses challenges for model performance. The correlation between in-distribution (ID) and OOD performance varies significantly, informing model selection based on application domains.

  • Enhancing PROTAC Ternary Complex Prediction with Ligand Information in AlphaFold 3
    Rocío, Mercado; Francisco, Erazo; Nils, Dunlop; Farzaneh, Jalalypour
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-06
     The paper discusses enhancing AlphaFold 3’s predictions of PROTAC ternary complexes by incorporating ligand information. Evaluating 48 PROTAC-related structures from the Protein Data Bank, the authors demonstrate improved accuracy using metrics like RMSD and pTM when ligand details are included. They also assess input strategies for better predictions, releasing their methodology and results on GitHub.
  • RIGR: Resonance Invariant Graph Representation for Molecular Property Prediction
    Akshat Shirish, Zalte; Hao-Wei, Pang; Anna C, Doner; William H., Green
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-06
     The paper introduces RIGR, a Resonance Invariant Graph Representation that improves molecular property predictions by creating a single representation for resonance-exhibiting molecules. Evaluated on a large dataset using the D-MPNN architecture, RIGR outperforms Chemprop while using 60% fewer features. It is available as an open-source featurization scheme, showing promise in various property prediction tasks.

06 Mar 2025

02 Mar 2025

  • Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data
    Johannes Kirchmair, Hosein Fooladi, Thi Ngoc Lan Vu
    Theoretical and Computational Chemistry on ChemRxiv
    2025-03-01
     This study evaluates twelve machine learning models for molecular property prediction, focusing on their performance on out-of-distribution (OOD) data using eight datasets and seven splitting strategies. It finds that while classical and GNN models perform similarly under scaffold-based splitting, cluster-based splitting presents more challenges. The correlation between in-distribution and OOD performance varies significantly, emphasizing careful model selection for specific applications.
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