AIMC Topic: Molecular Dynamics Simulation

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Accelerated Missense Mutation Identification in Intrinsically Disordered Proteins Using Deep Learning.

Biomacromolecules Mar 12, 2025
We use a combination of Brownian dynamics (BD) simulation results and deep learning (DL) strategies for the rapid identification of large structural changes caused by missense mutations in intrinsically disordered proteins (IDPs). We used ∼6500 IDP s...
Molecular Dynamics Simulation Intrinsically Disordered Proteins Mutation, Missense Algorithms Deep Learning Humans Neural Networks, Computer
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Identification of Novel Fourth-Generation Allosteric Inhibitors Targeting Inactive State of EGFR T790M/L858R/C797S and T790M/L858R Mutations: A Combined Machine Learning and Molecular Dynamics Approach.

The journal of physical chemistry. B Mar 7, 2025
Targeted therapy with an allosteric inhibitor (AIs) is an important area of research in patients with epidermal growth factor receptor (EGFR) mutations. Current treatment of nonsmall cell lung cancer patients with EGFR mutations using orthosteric inh...
Mutation Molecular Dynamics Simulation Quantitative Structure-Activity Relationship Humans Machine Learning Allosteric Regulation ErbB Receptors Protein Kinase Inhibitors
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Geometry-encoded molecular dynamics enables deep learning insights into P450 regiospecificity control.

Scientific reports Mar 3, 2025
Cytochrome P450 1A2, as many isoenzymes, can generate multiple metabolites from a single substrate. A loose coupling between substrate binding and oxygen activation makes possible substrate reorientations at the active site prior to catalysis. In the...
Substrate Specificity Cytochrome P-450 CYP1A2 Oxidation-Reduction Oxygen Catalytic Domain Caffeine Molecular Dynamics Simulation Humans Deep Learning
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Targeting Bacterial RNA Polymerase: Harnessing Simulations and Machine Learning to Design Inhibitors for Drug-Resistant Pathogens.

Biochemistry Feb 27, 2025
The increase in antimicrobial resistance presents a major challenge in treating bacterial infections, underscoring the need for innovative drug discovery approaches and novel inhibitors. Bacterial RNA polymerase (RNAP) has emerged as a crucial target...
Bacteria Bacterial Proteins Anti-Bacterial Agents Enzyme Inhibitors Drug Resistance, Bacterial Molecular Dynamics Simulation Humans Drug Design DNA-Directed RNA Polymerases Machine Learning
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Food-derived DPP4 inhibitors: Drug discovery based on high-throughput virtual screening and deep learning.

Food chemistry Feb 21, 2025
Dipeptidyl peptidase-4 (DPP-4) is a critical target for the treatment of type 2 diabetes. This study outlines the development of six compounds derived from food sources and modified to create promising candidates for the treatment of diabetes. These ...
Humans Drug Discovery Deep Learning High-Throughput Screening Assays Dipeptidyl Peptidase 4 Dipeptidyl-Peptidase IV Inhibitors Hypoglycemic Agents Molecular Dynamics Simulation Diabetes Mellitus, Type 2
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Sampling Conformational Ensembles of Highly Dynamic Proteins via Generative Deep Learning.

Journal of chemical information and modeling Feb 21, 2025
Proteins are inherently dynamic, and their conformational ensembles play a crucial role in biological function. Large-scale motions may govern the protein structure-function relationship, and numerous transient but stable conformations of intrinsical...
Peptide Fragments Deep Learning Protein Conformation Amyloid beta-Peptides Molecular Dynamics Simulation Intrinsically Disordered Proteins
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Deep learning-driven semi-rational design in phenylalanine ammonia-lyase for enhanced catalytic efficiency.

International journal of biological macromolecules Feb 19, 2025
Phenylalanine ammonia-lyase (PAL) possesses significant potential in agriculture, industry, and the treatment of various diseases, including cancer. In particular, PAL derived from Anabaena variabilis (AvPAL) has been successfully utilized in clinica...
Protein Engineering Phenylalanine Ammonia-Lyase Anabaena variabilis Molecular Dynamics Simulation Biocatalysis Catalysis Mutation Kinetics Deep Learning
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Identification of potent phytochemicals against Magnaporthe oryzae through machine learning aided-virtual screening and molecular dynamics simulation approach.

Computers in biology and medicine Feb 17, 2025
Magnaporthe oryzae stands as a notorious fungal pathogen responsible for causing devastating blast disease in cereals, leading to substantial reductions in grain production. Despite the usage of chemical fungicides to combat the pathogen, their effec...
Ascomycota Magnaporthe Plant Diseases Fungal Proteins Machine Learning Molecular Docking Simulation Fungicides, Industrial Molecular Dynamics Simulation Phytochemicals
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Accurate Prediction of ωB97X-D/6-31G* Equilibrium Geometries from a Neural Net Starting from Merck Molecular Force Field (MMFF) Molecular Mechanics Geometries.

Journal of chemical information and modeling Feb 17, 2025
Starting from Merck Molecular Force Field (MMFF) geometries, a neural-net based model has been formulated to closely reproduce ωB97X-D/6-31G* equilibrium geometries for organic molecules. The model involves training to >6 million energy and force cal...
Biological Products Molecular Dynamics Simulation Molecular Conformation Neural Networks, Computer
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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 ...
Humans Machine Learning Molecular Structure Molecular Dynamics Simulation Molecular Docking Simulation Thermodynamics Quantum Theory Histone Deacetylase Inhibitors Quantitative Structure-Activity Relationship Histone Deacetylases
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