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Thermodynamics

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SchNet_IIA: Potential Energy Surface Fitting by Interatomic Interactions Attention Based on Transfer Learning Analysis.

Journal of chemical information and modeling 39801289
Machine learning methods for fitting potential energy surfaces and molecular dynamics simulations are becoming increasingly popular due to their potentially high accuracy and savings in computational resources. However, existing application models of...
Machine Learning Molecular Dynamics Simulation Neural Networks, Computer Surface Properties Thermodynamics
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AlphaMut: A Deep Reinforcement Learning Model to Suggest Helix-Disrupting Mutations.

Journal of chemical theory and computation 39702999
Helices are important secondary structural motifs within proteins and are pivotal in numerous physiological processes. While amino acids (AA) such as alanine and leucine are known to promote helix formation, proline and glycine disfavor it. Helical s...
Deep Learning Models, Molecular Mutation Protein Structure, Secondary Proteins Reinforcement Machine Learning Thermodynamics
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An integrated machine learning approach delineates an entropic expansion mechanism for the binding of a small molecule to α-synuclein.

eLife 39693390
The mis-folding and aggregation of intrinsically disordered proteins (IDPs) such as α-synuclein (αS) underlie the pathogenesis of various neurodegenerative disorders. However, targeting αS with small molecules faces challenges due to the lack of defi...
1-(5-Isoquinolinesulfonyl)-2-Methylpiperazine alpha-Synuclein Entropy Humans Intrinsically Disordered Proteins Machine Learning Protein Binding Protein Conformation Thermodynamics
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Machine-Learning-Enabled Thermochemistry Estimator.

Journal of chemical information and modeling 39680848
Modeling adsorbates on single-crystal metals is critical in rational catalyst design and other research that requires detailed thermochemistry. First-principles simulations via density functional theory (DFT) are among the prevalent tools to acquire ...
Density Functional Theory Machine Learning Thermodynamics
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Design of Recyclable Plastics with Machine Learning and Genetic Algorithm.

Journal of chemical information and modeling 39625382
We present an artificial intelligence-guided approach to design durable and chemically recyclable ring-opening polymerization (ROP) class polymers. This approach employs a genetic algorithm (GA) that designs new monomers and then utilizes virtual for...
Algorithms Machine Learning Plastics Polymerization Polystyrenes Recycling Thermodynamics
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Machine Learning Quantum Mechanical/Molecular Mechanical Potentials: Evaluating Transferability in Dihydrofolate Reductase-Catalyzed Reactions.

Journal of chemical theory and computation 39815393
Integrating machine learning potentials (MLPs) with quantum mechanical/molecular mechanical (QM/MM) free energy simulations has emerged as a powerful approach for studying enzymatic catalysis. However, its practical application has been hindered by t...
Biocatalysis Machine Learning Molecular Dynamics Simulation Quantum Theory Tetrahydrofolate Dehydrogenase Thermodynamics
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CPconf_score: A Deep Learning Free Energy Function Trained Using Molecular Dynamics Data for Cyclic Peptides.

Journal of chemical theory and computation 39801200
Accurate structural feature characterization of cyclic peptides (CPs), especially those with less than 10 residues and -peptide bonds, is challenging but important for the rational design of bioactive peptides. In this study, we performed high-temper...
Deep Learning Molecular Dynamics Simulation Peptides, Cyclic Protein Conformation Thermodynamics
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Graph-Based Deep Learning Models for Thermodynamic Property Prediction: The Interplay between Target Definition, Data Distribution, Featurization, and Model Architecture.

Journal of chemical information and modeling 39780656
In this contribution, we examine the interplay between target definition, data distribution, featurization approaches, and model architectures on graph-based deep learning models for thermodynamic property prediction. Through consideration of five cu...
Deep Learning Thermodynamics
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Evaluation of Machine Learning/Molecular Mechanics End-State Corrections with Mechanical Embedding to Calculate Relative Protein-Ligand Binding Free Energies.

Journal of chemical theory and computation 39753520
The development of machine-learning (ML) potentials offers significant accuracy improvements compared to molecular mechanics (MM) because of the inclusion of quantum-mechanical effects in molecular interactions. However, ML simulations are several ti...
Ligands Machine Learning Molecular Dynamics Simulation Protein Binding Proteins Quantum Theory Thermodynamics
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On the emergence of machine-learning methods in bottom-up coarse-graining.

Current opinion in structural biology 39752847
Machine-learning methods have gained significant attention in the computational chemistry community as a viable approach to molecular modeling and analysis. Recent successes in utilizing neural networks to learn atomistic force-fields which 'coarse-g...
Machine Learning Models, Molecular Molecular Dynamics Simulation Neural Networks, Computer Thermodynamics
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