AIMC Topic: Thermodynamics

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Physics-Embedded Machine Learning Model for Phase Equilibrium Prediction in Multicomponent Systems.

Journal of chemical information and modeling Sep 22, 2025
We present TeNNet-SAC (hermodynamics-mbedded eural work for egment ctivity oefficient) model, a novel machine learning framework for predicting activity coefficients in liquid mixtures using only the SMILES representations of the constituent molecule...
Machine Learning Quantum Theory Thermodynamics Neural Networks, Computer
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Predicting Saturation Concentrations of Phase-Separating Proteins via Thermodynamic Integration.

Journal of chemical theory and computation Sep 19, 2025
Phase separation of proteins and nucleic acids into biomolecular condensates contributes to the regulation of cellular compartmentalization in membrane-less environments. A key parameter controlling the onset of biomolecular condensate formation is t...
Molecular Dynamics Simulation Thermodynamics Humans Proteins Biomolecular Condensates
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Mechanism-Driven Features Enable Asn Deamidation Reactivity Prediction via Machine Learning Methods.

Journal of chemical information and modeling Sep 19, 2025
The spontaneous deamidation of Asparagine (Asn) residues is a common post-translational modification of proteins that can occur on disparate time scales, ranging from hours to thousands of years. This variability in the reaction rate reflects the inf...
Molecular Dynamics Simulation Machine Learning Hydrogen Bonding Asparagine Amides Thermodynamics
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Integrating Machine Learning into Free Energy Perturbation Workflows.

Journal of chemical information and modeling Sep 17, 2025
Free energy perturbation (FEP) methods are among the most accurate tools in structure-based drug design for predicting protein-ligand binding affinities. However, their adoption remains limited due to high computational demands and complex setup proc...
Thermodynamics Drug Discovery Proteins Workflow Ligands Machine Learning Drug Design Humans
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Employing deep mutational scanning in the periplasm to decode the thermodynamic landscape for amyloid formation.

Proceedings of the National Academy of Sciences of the United States of America Sep 17, 2025
Deep mutational scanning (DMS) assays provide a powerful method to generate large-scale datasets essential for advancing AI-driven predictions in biology. The tripartite β-lactamase assay (TPBLA), in which a protein of interest is inserted between tw...
Escherichia coli Amyloid beta-Peptides Humans beta-Lactamases Alzheimer Disease Amyloid Peptide Fragments Mutation Thermodynamics Periplasm
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Hierarchical AF2RAVE for Multiconformation Virtual Screening Targeting S100 Ca-Binding Proteins.

Journal of chemical theory and computation Sep 4, 2025
Protein function is driven by transitions between metastable conformations, many of which are not conserved across homologues, offering opportunities for selective drug design. Accurately modeling both backbone and side chain metastability, and gener...
Thermodynamics Machine Learning Molecular Docking Simulation Calcium Humans S100 Proteins Protein Conformation
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Intelligent Design of Terminators by Coupling Prediction and Generation Models.

ACS synthetic biology Sep 4, 2025
Terminators are specific nucleotide sequences located at the 3' end of a gene and contain transcription termination information. As a fundamental genetic regulatory element, terminators play a crucial role in the design of gene circuits. Accurately c...
Machine Learning Models, Genetic Escherichia coli Terminator Regions, Genetic Synthetic Biology Thermodynamics
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3D Spatial Learning for Adsorption Energy Prediction in Multi-Temporal Solution Systems: The MTSS Data Set and a GCN-Based Network.

Journal of chemical information and modeling Sep 3, 2025
Existing methods for adsorption energy prediction primarily focus on individual molecules or static molecular pairs, lacking the capabilities to model the diverse spatial configurations found in complex solution systems. While traditional data sets a...
Thermodynamics Deep Learning Molecular Conformation Adsorption Solutions Solvents
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Explainable machine learning for comprehensive characterization of poly (6-(Ethoxybenzothiazole acrylamide)) resin for removal of Th(IV), As(V), and Hg(II) ions from aqueous solution.

Environmental geochemistry and health Aug 31, 2025
Adsorption is a promising technique with significant potential for water purification. In this context, the present study examines the adsorption efficiency of poly(6-(ethoxybenzothiazole acrylamide) (PEBTA) in removing high-valent metal ions from aq...
Adsorption Kinetics Water Purification Machine Learning Mercury Thermodynamics Water Pollutants, Chemical Benzothiazoles
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Chemical Space Exploration and Reinforcement Learning for Discovery of Novel Benzimidazole Hybrid Antibiotics.

Journal of chemical information and modeling Aug 27, 2025
Benzimidazole hybrids are promising antibacterial agents, but the growing problem of antibiotic resistance has led to the necessity of developing novel compounds with enhanced antimicrobial activity. This study utilizes AI methods to generate new ant...
Microbial Sensitivity Tests Molecular Docking Simulation Machine Learning Thermodynamics Benzimidazoles Anti-Bacterial Agents Drug Discovery
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