AIMC Topic: Molecular Dynamics Simulation

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Optimizing kinase and PARP inhibitor combinations through machine learning and in silico approaches for targeted brain cancer therapy.

Molecular diversity Jan 22, 2025
The drug combination is an attractive approach for cancer treatment. PARP and kinase inhibitors have recently been explored against cancer cells, but their combination has not been investigated comprehensively. In this study, we used various drug com...
Humans Poly(ADP-ribose) Polymerase Inhibitors Lapatinib Molecular Dynamics Simulation Piperidines Protein Kinase Inhibitors Indazoles Molecular Targeted Therapy Blood-Brain Barrier Brain Neoplasms Molecular Docking Simulation Machine Learning Computer Simulation
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Identifying candidate RNA-seq biomarkers for severity discrimination in chemical injuries: A machine learning and molecular dynamics approach.

International immunopharmacology Jan 22, 2025
INTRODUCTION: Biomarkers play a crucial role across various fields by providing insights into biological responses to interventions. High-throughput gene expression profiling technologies facilitate the discovery of data-driven biomarkers through ext...
Algorithms Molecular Dynamics Simulation Humans Gene Expression Profiling Mustard Gas Machine Learning Biomarkers RNA-Seq Chemical Warfare Agents Male
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Machine Learning Quantum Mechanical/Molecular Mechanical Potentials: Evaluating Transferability in Dihydrofolate Reductase-Catalyzed Reactions.

Journal of chemical theory and computation Jan 15, 2025
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...
Tetrahydrofolate Dehydrogenase Quantum Theory Biocatalysis Thermodynamics Machine Learning Molecular Dynamics Simulation
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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 Jan 13, 2025
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 Protein Conformation Thermodynamics Peptides, Cyclic
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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...
Humans Molecular Docking Simulation Inflammation Machine Learning Molecular Dynamics Simulation Support Vector Machine Protein Kinase Inhibitors Drug Repositioning src-Family Kinases
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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...
Molecular Dynamics Simulation Hydrolases Protein Engineering Artificial Intelligence Kinetics
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Integrating machine learning and structural dynamics to explore B-cell lymphoma-2 inhibitors for chronic lymphocytic leukemia therapy.

Molecular diversity Jan 9, 2025
Chronic lymphocytic leukemia (CLL) is a malignancy caused by the overexpression of the anti-apoptotic protein B-cell lymphoma-2 (BCL-2), making it a critical therapeutic target. This study integrates computational screening, molecular docking, and mo...
Humans Machine Learning Leukemia, Lymphocytic, Chronic, B-Cell Protein Binding Molecular Dynamics Simulation Molecular Docking Simulation Proto-Oncogene Proteins c-bcl-2 Antineoplastic Agents
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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...
RNA Nucleic Acid Conformation Peptides Deep Learning RNA, Viral Molecular Dynamics Simulation Protein Binding
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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...
Machine Learning Oxygen Molecular Dynamics Simulation Desulfovibrio Hydrogenase Hydrogen Carbon Monoxide
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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 Peptides Humans Hordeum Molecular Docking Simulation Angiotensin-Converting Enzyme Inhibitors Machine Learning Peptidyl-Dipeptidase A Antihypertensive Agents
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