ArticleJournal of chemical information and modeling2021
Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning.
Article in Journal of chemical information and modeling, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.
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Who cites it
38 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Anti-Obesity Therapeutic Targets Studied In Silico and In Vivo: A Systematic Review.International journal of molecular sciences · 2024Pooled it
- An integrated computational strategy for profiling terpenoid for dual-target leads against Klebsiella pneumoniae penicillin-binding protein 3 and beta-lactamase.Molecular diversity · 2026Article
- PL-PatchSurfer3: improved structure-based virtual screening for structure variation using 3D Zernike descriptors.Journal of cheminformatics · 2026Article
- Drug Repurposing Uncovers New Chemical Scaffolds as Potent Urease Inhibitors: A Comprehensive Computational Study.International journal of molecular sciences · 2026Article
- Computational Identification of Potential Novel Allosteric IHF Inhibitors Using QSAR Modeling to Inhibit Plasmid-Mediated Antibiotic Resistance.International journal of molecular sciences · 2026Article
- Revealing the Binding Mechanism of Gossypol on Bcl‑2 via Funnel Metadynamics Simulations.ACS omega · 2026Article
- Integrating multi-structure covalent docking with machine-learning consensus scoring enhances potency ranking of human acetylcholinesterase inhibitors.Briefings in bioinformatics · 2026Article
- Stability-Driven Pose Prediction and Ligand Design via Contact Persistence Analysis from Molecular Dynamics Simulations.Computational and structural biotechnology journal · 2026Article
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- Article
- Using Deep Graph Neural Networks Improves Physics-Based Hydration Free Energy Predictions Even for Molecules Outside of the Training Set Distribution.The journal of physical chemistry. B · 2025Article
- Digital Alchemy: The Rise of Machine and Deep Learning in Small-Molecule Drug Discovery.International journal of molecular sciences · 2025Review
- Ensemble Docking for Intrinsically Disordered Proteins.Journal of chemical information and modeling · 2025Article
- Integrating Hydrogen Exchange with Molecular Dynamics for Improved Ligand Binding Predictions.Journal of chemical information and modeling · 2025Article
- SPLIF-Enhanced Attention-Driven 3D CNNs for Precise and Reliable Protein-Ligand Interaction Modeling for METTL3.ACS omega · 2025Article
- Can Deep Learning Blind Docking Methods be Used to Predict Allosteric Compounds?Journal of chemical information and modeling · 2025Article
- Toward the Prediction of Binding Events in Very Flexible, Allosteric, Multidomain Proteins.Journal of chemical information and modeling · 2025Article
- SurfDock is a surface-informed diffusion generative model for reliable and accurate protein-ligand complex prediction.Nature methods · 2025Article
- Ensemble docking for intrinsically disordered proteins.bioRxiv : the preprint server for biology · 2025Article
- In Silico Conotoxin Studies: Progress and Prospects.Molecules (Basel, Switzerland) · 2024Review
Corrections and comments
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4 authors.
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Abstract
One of the main challenges of structure-based virtual screening (SBVS) is the incorporation of the receptor's flexibility, as its explicit representation in every docking run implies a high computational cost. Therefore, a common alternative to include the receptor's flexibility is the approach known as ensemble docking. Ensemble docking consists of using a set of receptor conformations and performing the docking assays over each of them. However, there is still no agreement on how to combine the ensemble docking results to obtain the final ligand ranking. A common choice is to use consensus strategies to aggregate the ensemble docking scores, but these strategies exhibit slight improvement regarding the single-structure approach. Here, we claim that using machine learning (ML) methodologies over the ensemble docking results could improve the predictive power of SBVS. To test this hypothesis, four proteins were selected as study cases: CDK2, FXa, EGFR, and HSP90. Protein conformational ensembles were built from crystallographic structures, whereas the evaluated compound library comprised up to three benchmarking data sets (DUD, DEKOIS 2.0, and CSAR-2012) and cocrystallized molecules. Ensemble docking results were processed through 30 repetitions of 4-fold cross-validation to train and validate two ML classifiers: logistic regression and gradient boosting trees. Our results indicate that the ML classifiers significantly outperform traditional consensus strategies and even the best performance case achieved with single-structure docking. We provide statistical evidence that supports the effectiveness of ML to improve the ensemble docking performance.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.