ArticleFrontiers in pharmacology2019
Machine Learning From Molecular Dynamics Trajectories to Predict Caspase-8 Inhibitors Against Alzheimer's Disease.
Article in Frontiers in pharmacology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Integrating computational chemistry and machine learning to predict KRAS mutation-induced resistance.bioRxiv : the preprint server for biology · 2026Article
- Exploring the mechanism of Platycladi Cacumen in intervening androgenetic alopecia based on network pharmacology, molecular docking, and molecular dynamics simulation.Scientific reports · 2026Article
- Spatio-temporal learning from molecular dynamics simulations for protein-ligand binding affinity prediction.Bioinformatics (Oxford, England) · 2025Article
- Elucidation of Artemisinin as a Potent GSK3β Inhibitor for Neurodegenerative Disorders via Machine Learning-Driven QSAR and Virtual Screening of Natural Compounds.Pharmaceuticals (Basel, Switzerland) · 2025Article
- AI-driven drug discovery and repurposing using multi-omics for myocardial infarction and heart failure.Exploration of medicine · 2025Article
- 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) · 2024Article
- Molecular dynamics and machine learning stratify motion-dependent activity profiles of S-layer destabilizing nanobodies.PNAS nexus · 2024Article
- Caspase-8 in inflammatory diseases: a potential therapeutic target.Cellular & molecular biology letters · 2024Review
- A Study on the Robustness and Stability of Explainable Deep Learning in an Imbalanced Setting: The Exploration of the Conformational Space of G Protein-Coupled Receptors.International journal of molecular sciences · 2024Article
- Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants.Journal of chemical information and modeling · 2024Article
- Systems pharmacology and multi-scale mechanism of Enicostema axillare bioactives in treating Alzheimer disease.Inflammopharmacology · 2024Article
- Advances in Developing Small Molecule Drugs for Alzheimer's Disease.Current Alzheimer research · 2024Review
- Unsupervised deep learning for molecular dynamics simulations: a novel analysis of protein-ligand interactions in SARS-CoV-2 MRSC advances · 2023Article
- Discovery of Nonretinoid Inhibitors of CRBP1: Structural and Dynamic Insights for Ligand-Binding Mechanisms.ACS chemical biology · 2023Article
- Identification of inhibitors for Agr quorum sensing system of Staphylococcus aureus by machine learning, pharmacophore modeling, and molecular dynamics approaches.Journal of molecular modeling · 2023Article
- Machine Learning Methods for Small Data Challenges in Molecular Science.Chemical reviews · 2023Review
- Alkaloid fromPharmaceuticals (Basel, Switzerland) · 2023Article
- Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.Molecular diversity · 2023Review
- Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods.Current opinion in structural biology · 2023Review
- AB-DB: Force-Field parameters, MD trajectories, QM-based data, and Descriptors of Antimicrobials.Scientific data · 2022Article
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3 authors.
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Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder in which the death of brain cells takes place leading to loss of memory and decreased cognitive ability. AD is a leading cause of death worldwide and is progressive in nature with symptoms worsening over time. Machine learning-based computational predictive models based on 2D and 3D descriptors have been effective in identifying potential active compounds. However, the use of data from molecular dynamics (MD) trajectories for training machine learning models still needs to be explored. In the present study, descriptors have been extracted from the MD trajectories of caspase-8 ligand complexes to train models using artificial neural networks and random forest algorithms. Caspase-8 plays a key role in causing AD by cleaving amyloid precursor proteins during apoptosis leading to increased formation of the amyloid-beta peptide. A total of 43 ligands were docked using the glide module of Schrodinger software, and short MD simulations of 10 ns were performed for the calculation of MD descriptors. The MD descriptors were also combined with the 2D and 3D descriptors of chemical compounds, and individual descriptor based as well as combination models were generated. This study demonstrated that MD descriptors could be effectively used for the characterization of bioactive compounds along with lead prioritization and optimization.
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