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Molecular Dynamics Simulation

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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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Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine Learning.

Molecules (Basel, Switzerland) 39795072
The src-homology 2 domain-containing phosphatase 2 (SHP2) is a human cytoplasmic protein tyrosine phosphatase that plays a crucial role in cellular signal transduction. Aberrant activation and mutations of SHP2 are associated with tumor growth and im...
Allosteric Regulation Enzyme Inhibitors Humans Machine Learning Molecular Dynamics Simulation Protein Binding Protein Conformation Protein Tyrosine Phosphatase, Non-Receptor Type 11
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Harnessing the Power of Machine Learning Guided Discovery of NLRP3 Inhibitors Towards the Effective Treatment of Rheumatoid Arthritis.

Cells 39791728
The NLRP3 inflammasome, plays a critical role in the pathogenesis of rheumatoid arthritis (RA) by activating inflammatory cytokines such as IL1β and IL18. Targeting NLRP3 has emerged as a promising therapeutic strategy for RA. In this study, a multid...
Antirheumatic Agents Arthritis, Rheumatoid Chemical Phenomena Databases, Chemical Machine Learning Molecular Dynamics Simulation NLR Family, Pyrin Domain-Containing 3 Protein Quantitative Structure-Activity Relationship
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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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Integrating machine learning and structure-based approaches for repurposing potent tyrosine protein kinase Src inhibitors to treat inflammatory disorders.

Scientific reports 39805859
Tyrosine-protein kinase Src plays a key role in cell proliferation and growth under favorable conditions, but its overexpression and genetic mutations can lead to the progression of various inflammatory diseases. Due to the specificity and selectivit...
Drug Repositioning Humans Inflammation Machine Learning Molecular Docking Simulation Molecular Dynamics Simulation Protein Kinase Inhibitors src-Family Kinases Support Vector Machine
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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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Biochemical and Computational Characterization of Haloalkane Dehalogenase Variants Designed by Generative AI: Accelerating the S2 Step.

Journal of the American Chemical Society 39792627
Generative artificial intelligence (AI) models trained on natural protein sequences have been used to design functional enzymes. However, their ability to predict individual reaction steps in enzyme catalysis remains unclear, limiting the potential u...
Artificial Intelligence Hydrolases Kinetics Molecular Dynamics Simulation Protein Engineering
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Enhanced Sampling Simulations of RNA-Peptide Binding Using Deep Learning Collective Variables.

Journal of chemical information and modeling 39772512
Enhanced sampling (ES) simulations of biomolecular recognition, such as binding small molecules to proteins and nucleic acid targets, protein-protein association, and protein-nucleic acid interactions, have gained significant attention in the simulat...
Deep Learning Molecular Dynamics Simulation Nucleic Acid Conformation Peptides Protein Binding RNA RNA, Viral
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PathInHydro, a Set of Machine Learning Models to Identify Unbinding Pathways of Gas Molecules in [NiFe] Hydrogenases.

Journal of chemical information and modeling 39764769
Machine learning (ML) is a powerful tool for the automated data analysis of molecular dynamics (MD) simulations. Recent studies showed that ML models can be used to identify protein-ligand unbinding pathways and understand the underlying mechanism. T...
Carbon Monoxide Desulfovibrio Hydrogen Hydrogenase Machine Learning Molecular Dynamics Simulation Oxygen
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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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