ArticleJournal of chemical information and modeling2020
Improving Protein-Ligand Docking Results with High-Throughput Molecular Dynamics Simulations.
Article in Journal of chemical information and modeling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 148 papers.
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Who cites it
148 citing papers in PubMed.
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- Virtual screening and molecular dynamics analysis of Plasmodium falciparum dihydroorotate dehydrogenase (DHODH) inhibitors targeting pyrimidine biosynthesis pathway.Folia microbiologica · 2026Article
- Exploring the mechanism of stigmasterol against androgenetic alopecia using geometry optimization, network pharmacology, molecular docking, and molecular dynamics studies.Scientific reports · 2026Article
- N-Substituted-2-(9H-Xanthen-9-yl)acetamide Derivatives Induce In Vitro Colon Cancer Cell Death via TASK-1 Inhibition: Lead Compounds for Further Optimization as TASK-1-Targeted Therapeutics in Colorectal Cancer.International journal of molecular sciences · 2026Article
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- Clove Essential Oil as an Antifungal Agent and Putative Dual-Action Antifungal Mechanism: Experimental Validation and Computational Insights into Orthosteric and Allosteric Modulation of Chitin Synthase I inMolecules (Basel, Switzerland) · 2026Article
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- In silico comparative proteomics and structural analysis of Theileria species reveals conserved druggable targets for anti-theilerial therapy.BMC microbiology · 2026Article
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88 more citing papers are in PubMed but not listed here.
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2 authors.
Funding
Abstract
Structure-based virtual screening relies on classical scoring functions that often fail to reliably discriminate binders from nonbinders. In this work, we present a high-throughput protein-ligand complex molecular dynamics (MD) simulation that uses the output from AutoDock Vina to improve docking results in distinguishing active from decoy ligands in a directory of useful decoy-enhanced (DUD-E) dataset. MD trajectories are processed by evaluating ligand-binding stability using root-mean-square deviations. We select 56 protein targets (of 7 different protein classes) and 560 ligands (280 actives, 280 decoys) and show 22% improvement in ROC AUC (area under the curve, receiver operating characteristics curve), from an initial value of 0.68 (AutoDock Vina) to a final value of 0.83. The MD simulation demonstrates a robust performance across all seven different protein classes. In addition, some predicted ligand-binding modes are moderately refined during MD simulations. These results systematically validate the reliability of a physics-based approach to evaluate protein-ligand binding interactions.
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