AIMC Topic: Thermodynamics

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Quantum Descriptor-Based Machine-Learning Modeling of Thermal Hazard of Cyclic Sulfamidates.

Journal of chemical information and modeling Aug 15, 2025
Cyclic sulfamidates are commonly used building blocks in organic synthesis. Correct classification of their thermal criticality is crucial for the safe use of these compounds in process development and scale-up. In this study, building on our earlier...
Temperature Sulfonamides Quantum Theory Thermodynamics Machine Learning
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Active Learning of Atomic Size Gas/Solid Potential Energy Surfaces via Physics Aware Models.

Journal of chemical information and modeling Aug 12, 2025
We propose an active learning (AL) framework to develop classical force fields (FFs) that accurately model the potential energy surfaces (PES) of gas/solid atomic-scale complexes. A central challenge is integrating AL with flexible, computationally e...
Silver Molecular Dynamics Simulation Machine Learning Density Functional Theory Monte Carlo Method Thermodynamics Surface Properties Gases
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ConfRank+: Extending Conformer Ranking to Charged Molecules.

Journal of chemical information and modeling Aug 7, 2025
We present a machine learning model for high-throughput energetic ranking of charged molecular conformers. Based on the ConfRank (Hölzer et al. , 8909-8925) approach, the model is trained in a pairwise fashion to predict energy differences for pair...
Thermodynamics Machine Learning Molecular Conformation
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Integrating Physics-Based Simulations with Data-Driven Deep Learning Represents a Robust Strategy for Developing Inhibitors Targeting the Main Protease.

Journal of chemical information and modeling Aug 6, 2025
The coronavirus main protease, essential for viral replication, is a well-validated antiviral target. Here, we present Deep-CovBoost, a computational pipeline integrating deep learning with free energy perturbation (FEP) simulations to guide the stru...
Deep Learning Antiviral Agents SARS-CoV-2 Drug Design Protease Inhibitors Coronavirus 3C Proteases Molecular Dynamics Simulation Thermodynamics Humans
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Machine Learning-Enhanced Calculation of Quantum-Classical Binding Free Energies.

Journal of chemical theory and computation Aug 5, 2025
Binding free energies are key elements in understanding and predicting the strength of protein-drug interactions. While classical free energy simulations yield good results for many purely organic ligands, drugs, including transition metal atoms, oft...
Quantum Theory Ligands Thermodynamics Protein Binding Molecular Dynamics Simulation Machine Learning Humans
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Limits on the computational expressivity of non-equilibrium biophysical processes.

Nature communications Aug 5, 2025
Many biological decision-making tasks require classifying high-dimensional chemical states. The biophysical and computational mechanisms that enable classification remain enigmatic. In this work, using Markov jump processes as an abstraction of gener...
Markov Chains Biophysical Phenomena Thermodynamics Computer Simulation Models, Biological Neural Networks, Computer
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Augmenting Chemical Databases for Atomistic Machine Learning by Sampling Conformational Space.

Journal of chemical information and modeling Aug 4, 2025
Machine learning (ML) has become a standard tool for the exploration of the chemical space. Much of the performance of such models depends on the chosen database for a given task. Here, this aspect is investigated for "chemical tasks" including the p...
Machine Learning Molecular Conformation Thermodynamics Models, Molecular Databases, Chemical
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Accurate VLE Predictions via COSMO-RS-Guided Deep Learning Models: Solubility and Selectivity in Physical Solvent Systems for Carbon Capture.

Journal of chemical information and modeling Aug 4, 2025
Carbon capture through physical solvents reduces energy consumption and lowers environmental impact compared with conventional chemical absorption methods. Typical properties for solvent screening are solubility and selectivity. However, they require...
Deep Learning Carbon Solubility Solvents Thermodynamics
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Thermodynamic analysis and intelligent modeling of statin drugs solubility in supercritical carbon dioxide.

Scientific reports Aug 2, 2025
Evaluating the solubility of various drugs in supercritical CO is a fundamental step in developing a supercritical process for formulating new pharmaceuticals. Atorvastatin, Lovastatin, and Simvastatin are statin drugs with limited solubility and low...
Solubility Thermodynamics Neural Networks, Computer Hydroxymethylglutaryl-CoA Reductase Inhibitors Simvastatin Machine Learning Atorvastatin Carbon Dioxide
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Navigating protein landscapes with a machine-learned transferable coarse-grained model.

Nature chemistry Jul 18, 2025
The most popular and universally predictive protein simulation models employ all-atom molecular dynamics, but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar pr...
Protein Folding Machine Learning Protein Conformation Proteins Thermodynamics Molecular Dynamics Simulation
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