ArticleScientific reports2017
Performance of machine-learning scoring functions in structure-based virtual screening.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 145 papers.
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Who cites it
145 citing papers in PubMed.
- DockM8: an all-in-one open-source platform for consensus virtual screening in drug design.Journal of cheminformatics · 2026Article
- Artificial Intelligence-Guided Phenotypic Drug Repurposing Against Streptococcus pneumoniae.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Enhancing virtual screening of cystathionine β-synthase inhibitors: benchmarking target-specific machine-learning scoring functions against state-of-the-art AI docking and co-folding approaches.Journal of cheminformatics · 2026Article
- Benchmarking docking and ML re-scoring screening performance for KRAS G12D in pancreatic cancer.Molecular diversity · 2026Article
- The Last Mile Problem: A Critical Assessment of Physics-Based and AI Tools for Small Molecule Binding Prediction in Virtual Screening.Journal of chemical information and modeling · 2026Article
- X-ray Co-crystal Structure of a Novel Pseudomonas aeruginosa DXPS Inhibitor Reveals an Unusual Allosteric Binding Pocket.Research square · 2026Article
- DrugBLIP: exploring the protein-molecule interaction mechanisms with a multi-task learning graph transformer.Bioinformatics (Oxford, England) · 2026Article
- AI-Enhanced Adaptive Virtual Screening Platform Enabling Exploration of 69 Billion Molecules Discovers Structurally Validated FSP1 Inhibitors.bioRxiv : the preprint server for biology · 2026Article
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- ActivityFinder: Toward the Fully Automatic Integration of Structural and Binding Affinity Data.Journal of chemical information and modeling · 2026Article
- CDK1 Signaling in Cancer Treatment: Advances in AI-based Strategies and Tools for New Cancer Drug Discovery.Combinatorial chemistry & high throughput screening · 2026Article
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- Computational exploration in search for novel natural product-derived EZH2 inhibitors for advancing anti-cancer therapy.Molecular diversity · 2025Article
- SCORCH2: A Generalized Heterogeneous Consensus Model for High-Enrichment Interaction-Based Virtual Screening.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- All That Glitters Is Not Gold: Importance of Rigorous Evaluation of Proteochemometric Models.Journal of chemical information and modeling · 2025Article
- Drug repurposing through Biophysical Insights: Focus on Indoleamine 2,3-Dioxygenase and Tryptophan 2,3-Dioxygenase Dual Inhibitors.Cell biochemistry and biophysics · 2025Article
- HERGAI: an artificial intelligence tool for structure-based prediction of hERG inhibitors.Journal of cheminformatics · 2025Article
- CACHE Challenge #2: Targeting the RNA Site of the SARS-CoV-2 Helicase Nsp13.Journal of chemical information and modeling · 2025Article
- A Database for Large-Scale Docking and Experimental Results.Journal of chemical information and modeling · 2025Article
85 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Classical scoring functions have reached a plateau in their performance in virtual screening and binding affinity prediction. Recently, machine-learning scoring functions trained on protein-ligand complexes have shown great promise in small tailored studies. They have also raised controversy, specifically concerning model overfitting and applicability to novel targets. Here we provide a new ready-to-use scoring function (RF-Score-VS) trained on 15 426 active and 893 897 inactive molecules docked to a set of 102 targets. We use the full DUD-E data sets along with three docking tools, five classical and three machine-learning scoring functions for model building and performance assessment. Our results show RF-Score-VS can substantially improve virtual screening performance: RF-Score-VS top 1% provides 55.6% hit rate, whereas that of Vina only 16.2% (for smaller percent the difference is even more encouraging: RF-Score-VS top 0.1% achieves 88.6% hit rate for 27.5% using Vina). In addition, RF-Score-VS provides much better prediction of measured binding affinity than Vina (Pearson correlation of 0.56 and -0.18, respectively). Lastly, we test RF-Score-VS on an independent test set from the DEKOIS benchmark and observed comparable results. We provide full data sets to facilitate further research in this area (http://github.com/oddt/rfscorevs) as well as ready-to-use RF-Score-VS (http://github.com/oddt/rfscorevs_binary).
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Registered trials
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.