AIMC Topic: Thermodynamics

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Predicting the diversity of photosynthetic light-harvesting using thermodynamics and machine learning.

PLoS computational biology Mar 11, 2025
Oxygenic photosynthesis is responsible for nearly all biomass production on Earth, and may have been a prerequisite for establishing a complex biosphere rich in multicellular life. Life on Earth has evolved to perform photosynthesis in a wide range o...
Thermodynamics Computational Biology Photosynthesis Light-Harvesting Protein Complexes Models, Biological Machine Learning Algorithms
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Discriminating High from Low Energy Conformers of Druglike Molecules: An Assessment of Machine Learning Potentials and Quantum Chemical Methods.

Chemphyschem : a European journal of chemical physics and physical chemistry Feb 27, 2025
Accurate and efficient prediction of high energy ligand conformations is important in structure-based drug discovery for the exclusion of unrealistic structures in docking-based virtual screening and de novo design approaches. In this work, we constr...
Molecular Conformation Thermodynamics Quantum Theory Ligands Density Functional Theory Pharmaceutical Preparations Machine Learning
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Estimating Absolute Protein-Protein Binding Free Energies by a Super Learner Model.

Journal of chemical information and modeling Feb 20, 2025
Protein-protein binding is central to most biochemical processes of all living beings. Its importance underlies mechanisms ranging from cell interactions to metabolic control, but also to biotechnology, such as the development of therapeutic monoclo...
Thermodynamics Protein Binding Proteins Machine Learning
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A semiempirical and machine learning approach for fragment-based structural analysis of non-hydroxamate HDAC3 inhibitors.

Biophysical chemistry Feb 15, 2025
Interest in HDAC3 inhibitors (HDAC3i) for pharmacological applications outside of cancer is growing. However, concerns regarding the possible mutagenicity of the commonly used hydroxamates (zinc-binding groups, ZBGs) are also increasing. Considering ...
Humans Machine Learning Quantitative Structure-Activity Relationship Thermodynamics Histone Deacetylases Histone Deacetylase Inhibitors Molecular Dynamics Simulation Quantum Theory Molecular Docking Simulation Molecular Structure
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Identification and understanding of allostery hotspots in proteins: Integration of deep mutational scanning and multi-faceted computational analyses.

Journal of molecular biology Feb 12, 2025
Motivated by recent deep mutational scanning (DMS) experiments, we have carried out a diverse set of computations to better understand the distribution and contributions of allostery hotspot residues in a transcription factor, TetR. These include ext...
Models, Molecular Mutation Thermodynamics Machine Learning Computational Biology Allosteric Site Molecular Dynamics Simulation Proteins Protein Binding Allosteric Regulation Protein Conformation
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Co-pyrolysis kinetics and enhanced synergy for furfural residues and polyethylene using artificial neural network and fast heating.

Waste management (New York, N.Y.) Feb 8, 2025
The efficient co-utilization of biomass and waste plastics is a key method to address the widely concerned environmental problem and replace traditional energy. Co-pyrolysis behaviors and synergistic effects of furfural residues (FR) and polyethylene...
Thermodynamics Pyrolysis Polyethylene Biomass Kinetics Furaldehyde Hot Temperature Neural Networks, Computer
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Scaling Graph Neural Networks to Large Proteins.

Journal of chemical theory and computation Feb 6, 2025
Graph neural network (GNN) architectures have emerged as promising force field models, exhibiting high accuracy in predicting complex energies and forces based on atomic identities and Cartesian coordinates. To expand the applicability of GNNs, and m...
Neural Networks, Computer Molecular Dynamics Simulation Thermodynamics Graph Neural Networks Machine Learning Proteins
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Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation.

Journal of chemical information and modeling Jan 28, 2025
In the field of computational chemistry, predicting bond dissociation energies (BDEs) presents well-known challenges, particularly due to the multireference character of reactive systems. Many chemical reactions involve configurations where single-re...
Quantum Theory Thermodynamics Machine Learning Methane
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Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks.

Journal of chemical information and modeling Jan 25, 2025
Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, le...
Graph Neural Networks Neural Networks, Computer Quantum Theory Machine Learning 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...
Machine Learning Protein Binding Humans DNA-Binding Proteins DNA Thermodynamics Mutation
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