AIMC Topic: Molecular Dynamics Simulation

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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 Jan 13, 2025
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 Molecular Dynamics Simulation src-Family Kinases Protein Kinase Inhibitors Molecular Docking Simulation Inflammation Support Vector Machine Humans Machine Learning
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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 Jan 10, 2025
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...
Hydrolases Protein Engineering Kinetics Molecular Dynamics Simulation Artificial Intelligence
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Enhanced Sampling Simulations of RNA-Peptide Binding Using Deep Learning Collective Variables.

Journal of chemical information and modeling Jan 7, 2025
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...
Peptides RNA, Viral Molecular Dynamics Simulation RNA Deep Learning Protein Binding Nucleic Acid Conformation
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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 Jan 7, 2025
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 Hydrogenase Oxygen Molecular Dynamics Simulation Machine Learning Hydrogen
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Machine learning discovery of novel antihypertensive peptides from highland barley protein inhibiting angiotensin I-converting enzyme (ACE).

Food research international (Ottawa, Ont.) Jan 6, 2025
Hypertension is a major global health concern, and there is a need for new antihypertensive agents derived from natural sources. This study aims to identify novel angiotensin I-converting enzyme (ACE) inhibitors from bioactive peptides derived from f...
Molecular Dynamics Simulation Plant Proteins Hordeum Peptidyl-Dipeptidase A Angiotensin-Converting Enzyme Inhibitors Peptides Molecular Docking Simulation Humans Antihypertensive Agents Machine 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...
Quantum Theory Machine Learning Ligands Protein Binding Thermodynamics Proteins Molecular Dynamics Simulation
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Unveiling key drivers of hepatocellular carcinoma: a synergistic approach with network pharmacology, machine learning-driven ligand discovery and dynamic simulations.

SAR and QSAR in environmental research Jan 3, 2025
Hepatocellular carcinoma (HCC) ranks fourth in cancer-related mortality worldwide. This study aims to uncover the genes and pathways involved in HCC through network pharmacology (NP) and to discover potential drugs via machine learning (ML)-based lig...
Glycogen Synthase Kinase 3 beta Carcinoma, Hepatocellular Network Pharmacology Proto-Oncogene Proteins c-akt Ligands Quantitative Structure-Activity Relationship Molecular Dynamics Simulation Molecular Docking Simulation Humans Drug Discovery Liver Neoplasms Machine Learning
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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...
Models, Molecular Molecular Dynamics Simulation Thermodynamics Neural Networks, Computer Machine Learning
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In silico design of dehydrophenylalanine containing peptide activators of glucokinase using pharmacophore modelling, molecular dynamics and machine learning: implications in type 2 diabetes.

Journal of computer-aided molecular design Dec 31, 2024
Diabetes represents a significant global health challenge associated with substantial healthcare costs and therapeutic complexities. Current diabetes therapies often entail adverse effects, necessitating the exploration of novel agents. Glucokinase (...
Molecular Dynamics Simulation Phenylalanine Glucokinase Enzyme Activators Pharmacophore Drug Design Diabetes Mellitus, Type 2 Hypoglycemic Agents Peptides Molecular Docking Simulation Humans Machine Learning Computer 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 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...
Neural Networks, Computer Machine Learning Molecular Dynamics Simulation Surface Properties Thermodynamics
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