AI Medical Compendium Journal:
Journal of chemical theory and computation

Showing 21 to 30 of 105 articles

AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics.

Journal of chemical theory and computation Nov 8, 2024
All-atom molecular simulations offer detailed insights into macromolecular phenomena, but their substantial computational cost hinders the exploration of complex biological processes. We introduce Advanced Machine-learning Atomic Representation Omni-...
Molecular Dynamics Simulation Thermodynamics Machine Learning Neural Networks, Computer Proteins
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Physics-Based Machine Learning Trains Hamiltonians and Decodes the Sequence-Conformation Relation in the Disordered Proteome.

Journal of chemical theory and computation Nov 6, 2024
Intrinsically disordered proteins and regions (IDPs) are involved in vital biological processes. To understand the IDP function, often controlled by conformation, we need to find the link between sequence and conformation. We decode this link by inte...
Machine Learning Proteome Protein Conformation Static Electricity Molecular Dynamics Simulation Intrinsically Disordered Proteins
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Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning.

Journal of chemical theory and computation Sep 25, 2024
We describe version 2 of the SPICE data set, a collection of quantum chemistry calculations for training machine learning potentials. It expands on the original data set by adding much more sampling of chemical space and more data on noncovalent inte...
Quantum Theory Thermodynamics Machine Learning Molecular Dynamics Simulation
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Enhanced Sampling of Biomolecular Slow Conformational Transitions Using Adaptive Sampling and Machine Learning.

Journal of chemical theory and computation Sep 20, 2024
Biomolecular simulations often suffer from the "time scale problem", hindering the study of rare events occurring over extended time scales. Enhanced sampling techniques aim to alleviate this issue by accelerating conformational transitions, yet they...
Dipeptides Protein Conformation Markov Chains Thermodynamics Alanine Machine Learning Molecular Dynamics Simulation
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A Mode Evolution Metric to Extract Reaction Coordinates for Biomolecular Conformational Transitions.

Journal of chemical theory and computation Sep 17, 2024
The complex, multidimensional energy landscape of biomolecules makes the extraction of suitable, nonintuitive collective variables (CVs) that describe their conformational transitions challenging. At present, dimensionality reduction approaches and m...
Machine Learning Molecular Dynamics Simulation Thermodynamics Dipeptides Protein Conformation Monte Carlo Method
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Machine Learning Prediction of Small Molecule Accumulation in Enhanced with Descriptor Statistics.

Journal of chemical theory and computation Jul 8, 2024
Antibiotic resistance, particularly among Gram-negative bacteria, poses a significant healthcare challenge due to their ability to evade antibiotic action through various mechanisms. In this study, we explore the prediction of small molecule accumula...
Small Molecule Libraries Machine Learning Anti-Bacterial Agents Escherichia coli Molecular Dynamics Simulation
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Application of Generative Artificial Intelligence in Predicting Membrane Partitioning of Drugs: Combining Denoising Diffusion Probabilistic Models and MD Simulations Reduces the Computational Cost to One-Third.

Journal of chemical theory and computation Jun 28, 2024
The optimal interaction of drugs with plasma membranes and membranes of subcellular organelles is a prerequisite for desirable pharmacology. Importantly, for drugs targeting the transmembrane lipid-facing sites of integral membrane proteins, the rela...
Molecular Dynamics Simulation Artificial Intelligence Diffusion Models, Statistical Pharmaceutical Preparations Cell Membrane Lipid Bilayers
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Machine Learning Derived Collective Variables for the Study of Protein Homodimerization in Membrane.

Journal of chemical theory and computation Jun 25, 2024
The accurate calculation of equilibrium constants for protein-protein association is of fundamental importance to quantitative biology and remains an outstanding challenge for computational biophysics. Traditionally, equilibrium constants have been c...
Thermodynamics Protein Multimerization Molecular Dynamics Simulation Machine Learning Membrane Proteins
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Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic Parameters.

Journal of chemical theory and computation Jun 18, 2024
Accurately describing long-range interactions is a significant challenge in molecular dynamics (MD) simulations of proteins. High-quality long-range potential is also an important component of the range-separated machine learning force field. This st...
Protein Conformation Molecular Dynamics Simulation Dipeptides Static Electricity Neural Networks, Computer Thermodynamics Proteins
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Quantum-to-Classical Neural Network Transfer Learning Applied to Drug Toxicity Prediction.

Journal of chemical theory and computation May 25, 2024
Toxicity is a roadblock that prevents an inordinate number of drugs from being used in potentially life-saving applications. Deep learning provides a promising solution to finding ideal drug candidates; however, the vastness of chemical space coupled...
Quantum Theory Machine Learning Neural Networks, Computer Deep Learning Drug-Related Side Effects and Adverse Reactions
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