Molecular Dynamics Simulation

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 39750056

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...

Carcinoma, Hepatocellular Drug Discovery Glycogen Synthase Kinase 3 beta Humans Ligands Liver Neoplasms Machine Learning Molecular Docking Simulation Molecular Dynamics Simulation Network Pharmacology Proto-Oncogene Proteins c-akt Quantitative Structure-Activity Relationship

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Machine learning-enabled virtual screening indicates the anti-tuberculosis activity of aldoxorubicin and quarfloxin with verification by molecular docking, molecular dynamics simulations, and biological evaluations.

Briefings in bioinformatics 39737570

Drug resistance in Mycobacterium tuberculosis (Mtb) is a significant challenge in the control and treatment of tuberculosis, making efforts to combat the spread of this global health burden more difficult. To accelerate anti-tuberculosis drug discove...

Antitubercular Agents Humans Machine Learning Microbial Sensitivity Tests Molecular Docking Simulation Molecular Dynamics Simulation Mycobacterium tuberculosis

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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 39739078

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 (...

Computer Simulation Diabetes Mellitus, Type 2 Drug Design Enzyme Activators Glucokinase Humans Hypoglycemic Agents Machine Learning Molecular Docking Simulation Molecular Dynamics Simulation Peptides Pharmacophore Phenylalanine

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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 39708597

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...

Algorithms Animals Endocrine Disruptors Environmental Pollutants Machine Learning Molecular Dynamics Simulation Neural Networks, Computer Organic Chemicals

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BioStructNet: Structure-Based Network with Transfer Learning for Predicting Biocatalyst Functions.

Journal of chemical theory and computation 39705058

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...

Algorithms Biocatalysis Deep Learning Enzymes Ligands Machine Learning Molecular Dynamics Simulation

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Advanced Mass-Spectra-Based Machine Learning for Predicting the Toxicity of Traditional Chinese Medicines.

Analytical chemistry 39704481

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...

Cytochrome P-450 CYP3A Drugs, Chinese Herbal Humans Machine Learning Medicine, Chinese Traditional Molecular Dynamics Simulation Quantitative Structure-Activity Relationship Spectrometry, Mass, Electrospray Ionization

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Enhanced prediction of beta-secretase inhibitory compounds with mol2vec technique and machine learning algorithms.

SAR and QSAR in environmental research 39704060

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...

Algorithms Amyloid Precursor Protein Secretases Enzyme Inhibitors Machine Learning Molecular Docking Simulation 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 39699247

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...

Algorithms Deep Learning Markov Chains Molecular Dynamics Simulation

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Grand canonical Monte Carlo and deep learning assisted enhanced sampling to characterize the distribution of Mg2+ and influence of the Drude polarizable force field on the stability of folded states of the twister ribozyme.

The Journal of chemical physics 39665326

Molecular dynamics simulations are crucial for understanding the structural and dynamical behavior of biomolecular systems, including the impact of their environment. However, there is a gap between the time scale of these simulations and that of rea...

Deep Learning Magnesium Molecular Dynamics Simulation Monte Carlo Method Nucleic Acid Conformation RNA, Catalytic

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On the emergence of machine-learning methods in bottom-up coarse-graining.

Current opinion in structural biology 39752847

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...

Machine Learning Models, Molecular Molecular Dynamics Simulation Neural Networks, Computer Thermodynamics

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AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Unveiling key drivers of hepatocellular carcinoma: a synergistic approach with network pharmacology, machine learning-driven ligand discovery and dynamic simulations.

Machine learning-enabled virtual screening indicates the anti-tuberculosis activity of aldoxorubicin and quarfloxin with verification by molecular docking, molecular dynamics simulations, and biological evaluations.

In silico design of dehydrophenylalanine containing peptide activators of glucokinase using pharmacophore modelling, molecular dynamics and machine learning: implications in type 2 diabetes.

Endocrine disruptor identification and multitoxicity level assessment of organic chemicals: An example of multiple machine learning models.

BioStructNet: Structure-Based Network with Transfer Learning for Predicting Biocatalyst Functions.

Advanced Mass-Spectra-Based Machine Learning for Predicting the Toxicity of Traditional Chinese Medicines.

Enhanced prediction of beta-secretase inhibitory compounds with mol2vec technique and machine learning algorithms.

Flow Matching for Optimal Reaction Coordinates of Biomolecular Systems.

Grand canonical Monte Carlo and deep learning assisted enhanced sampling to characterize the distribution of Mg2+ and influence of the Drude polarizable force field on the stability of folded states of the twister ribozyme.

On the emergence of machine-learning methods in bottom-up coarse-graining.

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