AIMC Topic: Thermodynamics

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Further Development of SAMPDI-3D: A Machine Learning Method for Predicting Binding Free Energy Changes Caused by Mutations in Either Protein or DNA.

Genes Jan 19, 2025
BACKGROUND/OBJECTIVES: Predicting the effects of protein and DNA mutations on the binding free energy of protein-DNA complexes is crucial for understanding how DNA variants impact wild-type cellular function. As many cellular interactions involve pro...
DNA-Binding Proteins Thermodynamics Mutation Humans Machine Learning Protein Binding DNA
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Machine Learning Quantum Mechanical/Molecular Mechanical Potentials: Evaluating Transferability in Dihydrofolate Reductase-Catalyzed Reactions.

Journal of chemical theory and computation Jan 15, 2025
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...
Tetrahydrofolate Dehydrogenase Thermodynamics Machine Learning Molecular Dynamics Simulation Quantum Theory Biocatalysis
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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 Jan 13, 2025
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 Protein Conformation Thermodynamics Molecular Dynamics Simulation Peptides, Cyclic
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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 Jan 9, 2025
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...
Thermodynamics Deep Learning
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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 Jan 3, 2025
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...
Thermodynamics Protein Binding Proteins Ligands Molecular Dynamics Simulation Machine Learning Quantum Theory
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On the emergence of machine-learning methods in bottom-up coarse-graining.

Current opinion in structural biology Jan 2, 2025
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...
Neural Networks, Computer Molecular Dynamics Simulation Thermodynamics Machine Learning Models, Molecular
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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 Dec 30, 2024
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 Neural Networks, Computer Surface Properties Molecular Dynamics Simulation Thermodynamics
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AlphaMut: A Deep Reinforcement Learning Model to Suggest Helix-Disrupting Mutations.

Journal of chemical theory and computation Dec 19, 2024
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...
Mutation Thermodynamics Models, Molecular Protein Structure, Secondary Deep Learning Reinforcement Machine Learning Proteins
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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 Dec 18, 2024
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...
Protein Conformation Thermodynamics Humans Entropy Machine Learning 1-(5-Isoquinolinesulfonyl)-2-Methylpiperazine alpha-Synuclein Protein Binding Intrinsically Disordered Proteins
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Machine-Learning-Enabled Thermochemistry Estimator.

Journal of chemical information and modeling Dec 16, 2024
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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