ArticleCommunications chemistry2024
Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning.
Article in Communications chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed.
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- Parabens exposure in systemic lupus erythematosus: insights from network toxicology and molecular docking.BMC pharmacology & toxicology · 2026Article
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- A Novel Drug Delivery System for the Treatment of Lupus Nephritis: From Delivery System Design and Optimization to Treatment.Biomolecules · 2026Article
- Mechanistic role of gut microbiota metabolites in hypertension-insomnia comorbidity via integrated network pharmacology and molecular dynamics.Scientific reports · 2026Article
- Integrated network toxicology suggests potential mechanisms involving environmental flame retardant TDCPP in ovarian cancer progression via "liver-to-ovary crosstalk."Environmental health : a global access science source · 2026Article
- Study on the Potential Molecular Mechanisms of Sodium Dehydroacetate (Na-DHA) Interfering With Bone Metabolism and Inducing Osteoporosis Based on Network Toxicology, Molecular Docking, and In Vitro Experimental Validation.Food science & nutrition · 2026Article
- Exploring the toxicological effects of Di-O-benzoyldiethylene glycol plasticizer on allergic rhinitis through network toxicology combined with molecular docking and molecular simulation.Scientific reports · 2026Article
- Deciphering DEL pocket patterns through contrastive learning.Nature communications · 2026Article
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Corrections and comments
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Authors and funding
2 authors.
Funding
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Abstract
Estimating protein targets of compounds based on the similarity principle-similar molecules are likely to show comparable bioactivity-is a long-standing strategy in drug research. Having previously quantified this principle, we present here a large-scale evaluation of its predictive power for inferring macromolecular targets by reverse screening an unprecedented vast external test set of more than 300,000 active small molecules against another bioactivity set of more than 500,000 compounds. We show that machine-learning can predict the correct targets, with the highest probability among 2069 proteins, for more than 51% of the external molecules. The strong enrichment thus obtained demonstrates its usefulness in supporting phenotypic screens, polypharmacology, or repurposing. Moreover, we quantified the impact of the bioactivity knowledge available for proteins in terms of number and diversity of actives. Finally, we advise that developers of such approaches follow an application-oriented benchmarking strategy and use large, high-quality, non-overlapping datasets as provided here.
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