AIMC Topic: Molecular Dynamics Simulation

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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...
Machine Learning Algorithms Molecular Docking Simulation Enzyme Inhibitors Amyloid Precursor Protein Secretases Molecular Dynamics Simulation Quantitative Structure-Activity Relationship
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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...
Neural Networks, Computer Machine Learning Animals Organic Chemicals Algorithms Endocrine Disruptors Environmental Pollutants Molecular Dynamics Simulation
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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...
Hydrocarbons, Brominated Trihalomethanes Machine Learning Molecular Dynamics Simulation Climate Change Lipid Bilayers Hydrophobic and Hydrophilic Interactions
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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...
Humans Artificial Intelligence Machine Learning Drug Discovery Molecular Dynamics Simulation
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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 Mutation Protein Engineering Molecular Dynamics Simulation Dioxygenases Lysine
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Generative adversarial network (GAN) model-based design of potent SARS-CoV-2 M inhibitors using the electron density of ligands and 3D binding pockets: insights from molecular docking, dynamics simulation, and MM-GBSA analysis.

Molecular diversity Nov 30, 2024
Deep learning-based generative adversarial network (GAN) frameworks have recently been developed to expedite the drug discovery process. These models generate novel molecules from scratch and validate them through molecular docking simulation to iden...
Protein Binding Molecular Dynamics Simulation Ligands Humans Deep Learning Molecular Docking Simulation SARS-CoV-2 Protease Inhibitors Antiviral Agents COVID-19 Drug Design COVID-19 Drug Treatment Coronavirus 3C Proteases Generative Adversarial Networks Binding Sites
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Rational design of potent phosphopeptide binders to endocrine Snk PBD domain by integrating machine learning optimization, molecular dynamics simulation, binding energetics rescoring, and in vitro affinity assay.

European biophysics journal : EBJ Nov 29, 2024
Human Snk is an evolutionarily conserved serine/threonine kinase essential for the maintenance of endocrine stability. The protein consists of a N-terminal catalytic domain and a C-terminal polo-box domain (PBD) that determines subcellular localizati...
Phosphopeptides Drug Design Protein Serine-Threonine Kinases Humans Amino Acid Sequence Protein Binding Molecular Dynamics Simulation Protein Domains Machine Learning Thermodynamics
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Machine Learning Models for Predicting Monoclonal Antibody Biophysical Properties from Molecular Dynamics Simulations and Deep Learning-Based Surface Descriptors.

Molecular pharmaceutics Nov 28, 2024
Monoclonal antibodies (mAbs) have found extensive applications and development in treating various diseases. From the pharmaceutical industry's perspective, the journey from the design and development of mAbs to clinical testing and large-scale produ...
Machine Learning Solubility Deep Learning Antibodies, Monoclonal Humans Viscosity Molecular Dynamics Simulation
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Augmenting Human Expertise in Weighted Ensemble Simulations through Deep Learning-Based Information Bottleneck.

Journal of chemical theory and computation Nov 26, 2024
The weighted ensemble (WE) method stands out as a widely used segment-based sampling technique renowned for its rigorous treatment of kinetics. The WE framework typically involves initially mapping the configuration space onto a low-dimensional colle...
Dipeptides Oligopeptides Deep Learning Molecular Dynamics Simulation Humans
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