ArticleProceedings of the National Academy of Sciences of the United States of America2020
Machine learning classification can reduce false positives in structure-based virtual screening.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers.
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Who cites it
96 citing papers in PubMed.
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- Structure-aware artificial intelligence for next-generation drug discovery: from protein-ligand modeling to generative biomolecular design.Briefings in bioinformatics · 2026Review
- Beyond Fungitoxicity: Recent Achievements in Targeted Fungicide Discovery.Journal of fungi (Basel, Switzerland) · 2026Review
- Structure-guided compound prioritization strategy for virtual screening identifies putative binders for the nuclear receptor LRH-1.bioRxiv : the preprint server for biology · 2026Article
- Rescuing the Function of Missense-Mutated Tumor SuppressorbioRxiv : the preprint server for biology · 2026Article
- Enabling Automatic Generation of Protein-Ligand Complex Data Sets with Atomistic Detail.Journal of chemical information and modeling · 2026Article
- PPIscreenML is a method for structure-based screening of protein-protein interactions using AlphaFold.eLife · 2026Article
- Integrative machine learning approach for identifying genes associated with quantitative traits: A soybean (Glycine max) yield case study.The plant genome · 2026Article
- Therapeutic monoclonal antibodies repurposing in oncology via IMGT/mAb-KG embeddings.BMC medical informatics and decision making · 2026Article
- Identifying Potential BACE1 Inhibitors from the ChEMBL Database Using Machine Learning and Atomistic Simulation Approaches.ACS omega · 2026Article
- Machine learning approaches for data-driven hydrocarbon bioaugmentation and phytoremediation: the role of multi-omics insights.Frontiers in microbiology · 2026Review
- E3 ubiquitin ligases: structural diversity, dysregulation in disease, and their emerging role in targeted therapeutic strategies.Frontiers in molecular biosciences · 2026Review
- Quo Vadis translational neuroscience?Translational neuroscience · 2026Review
- Coumarin benzamide scaffold as potential 5-HTJournal of molecular modeling · 2025Article
- Machine Learning Approaches for Optimizing Drug Combinations in Neurodegenerative Diseases: A Brief Review.ACS omega · 2025Review
- Molecular Modeling and Analysis of Cannabinoid and Cannabinoid-like Molecules Combining K-Means Clustering with Pearson Correlation and PCA.International journal of molecular sciences · 2025Article
- 3d electron cloud descriptors for enhanced QSAR modeling of anti-colorectal cancer compounds.Journal of computer-aided molecular design · 2025Article
- Unveiling Novel Arginase Inhibitors for Cutaneous Leishmaniasis Using Drug Repurposing and Virtual Screening Approaches.Journal of cellular biochemistry · 2025Article
36 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
With the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discovery's search for active chemical matter. In typical virtual screens, however, only about 12% of the top-scoring compounds actually show activity when tested in biochemical assays. We argue that most scoring functions used for this task have been developed with insufficient thoughtfulness into the datasets on which they are trained and tested, leading to overly simplistic models and/or overtraining. These problems are compounded in the literature because studies reporting new scoring methods have not validated their models prospectively within the same study. Here, we report a strategy for building a training dataset (D-COID) that aims to generate highly compelling decoy complexes that are individually matched to available active complexes. Using this dataset, we train a general-purpose classifier for virtual screening (vScreenML) that is built on the XGBoost framework. In retrospective benchmarks, our classifier shows outstanding performance relative to other scoring functions. In a prospective context, nearly all candidate inhibitors from a screen against acetylcholinesterase show detectable activity; beyond this, 10 of 23 compounds have IC
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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.