ArticleProtein science : a publication of the Protein Society2024
Benchmarking reverse docking through AlphaFold2 human proteome.
Article in Protein science : a publication of the Protein Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Improving AlphaFold2 Performance in Virtual Screens Targeting GPCRs by Enhancing Binding-Site Conformational Sampling.Journal of chemical information and modeling · 2026Article
- Exploring the Scoring Function Space with Lasso Regression.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Using AlphaFold and Symmetrical Docking to Predict Protein-Protein Interactions for Exploring Potential Crystallization Conditions.Proteins · 2025Article
- Harnessing AI-driven reverse docking in drug discovery: a comprehensive review of opportunities, challenges, and emerging trends.Journal of molecular modeling · 2025Review
- Review
- From Traditional Efficacy to Drug Design: A Review of Astragali Radix.Pharmaceuticals (Basel, Switzerland) · 2025Review
- OpenDock: a pytorch-based open-source framework for protein-ligand docking and modelling.Bioinformatics (Oxford, England) · 2024Article
- Benchmarking reverse docking through AlphaFold2 human proteome.Protein science : a publication of the Protein Society · 2024Article
- Protein language models are performant in structure-free virtual screening.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Predicting the binding of ligands to the human proteome via reverse-docking methods enables the understanding of ligand's interactions with potential protein targets in the human body, thereby facilitating drug repositioning and the evaluation of potential off-target effects or toxic side effects of drugs. In this study, we constructed 11 reverse docking pipelines by integrating site prediction tools (PointSite and SiteMap), docking programs (Glide and AutoDock Vina), and scoring functions (Glide, Autodock Vina, RTMScore, DeepRMSD, and OnionNet-SFCT), and then thoroughly benchmarked their predictive capabilities. The results show that the Glide_SFCT (PS) pipeline exhibited the best target prediction performance based on the atomic structure models in AlphaFold2 human proteome. It achieved a success rate of 27.8% when considering the top 100 ranked prediction. This pipeline effectively narrows the range of potential targets within the human proteome, laying a foundation for drug target prediction, off-target assessment, and toxicity prediction, ultimately boosting drug development. By facilitating these critical aspects of drug discovery and development, our work has the potential to ultimately accelerate the identification of new therapeutic agents and improve drug safety.
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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.