AIMC Topic: Molecular Dynamics Simulation

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Harnessing the Power of Machine Learning Guided Discovery of NLRP3 Inhibitors Towards the Effective Treatment of Rheumatoid Arthritis.

Cells Dec 30, 2024
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...
Databases, Chemical NLR Family, Pyrin Domain-Containing 3 Protein Machine Learning Quantitative Structure-Activity Relationship Molecular Dynamics Simulation Arthritis, Rheumatoid Chemical Phenomena Antirheumatic Agents
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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) Dec 24, 2024
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...
Enzyme Inhibitors Protein Conformation Allosteric Regulation Protein Binding Protein Tyrosine Phosphatase, Non-Receptor Type 11 Humans Molecular Dynamics Simulation Machine Learning
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BioStructNet: Structure-Based Network with Transfer Learning for Predicting Biocatalyst Functions.

Journal of chemical theory and computation Dec 20, 2024
Enzyme-substrate interactions are essential to both biological processes and industrial applications. Advanced machine learning techniques have significantly accelerated biocatalysis research, revolutionizing the prediction of biocatalytic activities...
Machine Learning Biocatalysis Enzymes Molecular Dynamics Simulation Algorithms Ligands Deep Learning
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Advanced Mass-Spectra-Based Machine Learning for Predicting the Toxicity of Traditional Chinese Medicines.

Analytical chemistry Dec 20, 2024
Traditional Chinese medicine (TCM) has been a cornerstone of health care for centuries, valued for its preventive and therapeutic properties. However, recent decades have revealed significant toxicological concerns associated with TCMs due to their c...
Quantitative Structure-Activity Relationship Spectrometry, Mass, Electrospray Ionization Cytochrome P-450 CYP3A Drugs, Chinese Herbal Molecular Dynamics Simulation Machine Learning Medicine, Chinese Traditional Humans
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Enhanced prediction of beta-secretase inhibitory compounds with mol2vec technique and machine learning algorithms.

SAR and QSAR in environmental research Dec 20, 2024
A comprehensive computational strategy that combined QSAR modelling, molecular docking, and ADMET analysis was used to discover potential inhibitors for β-secretase 1 (BACE-1). A dataset of 1,138 compounds with established BACE-1 inhibitory activitie...
Amyloid Precursor Protein Secretases Quantitative Structure-Activity Relationship Molecular Dynamics Simulation Enzyme Inhibitors Machine Learning Algorithms Molecular Docking Simulation
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Flow Matching for Optimal Reaction Coordinates of Biomolecular Systems.

Journal of chemical theory and computation Dec 19, 2024
We present flow matching for reaction coordinates (FMRC), a novel deep learning algorithm designed to identify optimal reaction coordinates (RC) in biomolecular reversible dynamics. FMRC is based on the mathematical principles of lumpability and deco...
Molecular Dynamics Simulation Markov Chains Algorithms Deep Learning
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Endocrine disruptor identification and multitoxicity level assessment of organic chemicals: An example of multiple machine learning models.

Journal of hazardous materials Dec 15, 2024
Endocrine-disrupting chemicals (EDCs) pollution is a major global environmental issue. Assessing the multiple toxic effects of EDCs is key to managing their risks. This study successfully developed an EDCs classification and recognition model based o...
Environmental Pollutants Molecular Dynamics Simulation Endocrine Disruptors Animals Organic Chemicals Machine Learning Algorithms Neural Networks, Computer
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Investigating the Bromoform Membrane Interactions Using Atomistic Simulations and Machine Learning: Implications for Climate Change Mitigation.

The journal of physical chemistry. B Dec 6, 2024
Methane emissions from livestock contribute to global warming. Seaweeds used as food additive offer a promising emission mitigation strategy because seaweeds are enriched in bromoform─a methanogenesis inhibitor. Therefore, understanding bromoform sto...
Machine Learning Climate Change Hydrocarbons, Brominated Lipid Bilayers Molecular Dynamics Simulation Hydrophobic and Hydrophilic Interactions Trihalomethanes
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Transforming drug discovery: the impact of AI and molecular simulation on R&D efficiency.

Bioanalysis Dec 6, 2024
The process of developing new drugs in the pharmaceutical industry is both time-consuming and costly, making efficiency crucial. Recent advances in hardware and computational methods have led to the widespread application of computational science app...
Drug Discovery Molecular Dynamics Simulation Artificial Intelligence Machine Learning Humans
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Machine Learning Guided Rational Design of a Non-Heme Iron-Based Lysine Dioxygenase Improves its Total Turnover Number.

Chembiochem : a European journal of chemical biology Dec 5, 2024
Highly selective C-H functionalization remains an ongoing challenge in organic synthetic methodologies. Biocatalysts are robust tools for achieving these difficult chemical transformations. Biocatalyst engineering has often required directed evolutio...
Machine Learning Iron Lysine Protein Engineering Dioxygenases Mutation Molecular Dynamics Simulation
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