AIMC Topic: Thermodynamics

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Current experimental, statistical, and mechanistic approaches to optimizing biomolecule separations in aqueous two-phase systems.

Journal of chromatography. A Mar 17, 2025
Aqueous two-phase systems (ATPS) have been used to purify a range of biomolecules, including small molecules, monoclonal antibodies, viruses, and whole cells. They are known for selective separations, creating a stabilizing, low-shear environment, an...
Water Models, Chemical Thermodynamics Models, Statistical Proteins
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Fast and Accurate Prediction of Tautomer Ratios in Aqueous Solution via a Siamese Neural Network.

Journal of chemical theory and computation Mar 16, 2025
Tautomerization plays a critical role in chemical and biological processes, influencing molecular stability, reactivity, biological activity, and ADME-Tox properties. Many drug-like molecules exist in multiple tautomeric states in aqueous solution, c...
Neural Networks, Computer Deep Learning Solutions Thermodynamics Water Isomerism
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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...
Light-Harvesting Protein Complexes Thermodynamics Photosynthesis Machine Learning Algorithms Computational Biology Models, Biological
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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...
Machine Learning Thermodynamics Density Functional Theory Ligands Molecular Conformation Pharmaceutical Preparations Quantum Theory
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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...
Protein Binding Machine Learning Proteins Thermodynamics
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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 ...
Molecular Structure Molecular Dynamics Simulation Quantum Theory Quantitative Structure-Activity Relationship Machine Learning Humans Histone Deacetylases Thermodynamics Molecular Docking Simulation Histone Deacetylase Inhibitors
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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...
Hot Temperature Pyrolysis Polyethylene Thermodynamics Kinetics Furaldehyde Biomass 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...
Proteins Molecular Dynamics Simulation Neural Networks, Computer Machine Learning Thermodynamics Graph Neural Networks
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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...
Thermodynamics Methane Quantum Theory Machine Learning
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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...
Thermodynamics Graph Neural Networks Neural Networks, Computer Machine Learning Quantum Theory
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