ArticleJournal of cheminformatics2018
P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure.
Article in Journal of cheminformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 259 papers.
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Who cites it
259 citing papers in PubMed.
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- Uncovering hidden druggable sites: computational approaches to allosteric and cryptic pocket discovery-from molecular dynamics to AI.Journal of computer-aided molecular design · 2026Review
- NRB-FusePS: A Protein Language Model-Based Multimodal Framework for Predicting Ligand-Binding Residues in Nuclear Receptors.The protein journal · 2026Article
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- Explainable AI reveals the allosteric blind spot in protein-ligand binding predictions.Cell reports. Physical science · 2026Article
- MOZAIC: compound growth via in silico reactions and global optimization using Conformational Space Annealing.Bioinformatics (Oxford, England) · 2026Article
- Pocket-PROTACs: an interpretable pocket-aware deep learning framework for predicting PROTAC-induced protein degradation.Bioinformatics (Oxford, England) · 2026Article
- Decoding the allosteric grammar of protein kinases: A dual-stream framework integrating protein language models and energy landscape frustration analysis.Protein science : a publication of the Protein Society · 2026Article
- Can Cavity Prediction Algorithms Help in Docking Experiments?Journal of computational chemistry · 2026Article
- CAFE: A Co-folding Approach for Fragment Exploration of Allosteric and Cryptic Binding Sites.bioRxiv : the preprint server for biology · 2026Article
- PockFlex: a web server for flexibility-aware binding site identification and prioritisation from structural ensembles.Nucleic acids research · 2026Article
- Integrating Molecular Similarity and AlphaFold-Based Structural Alignment for Target Discovery inPharmaceuticals (Basel, Switzerland) · 2026Article
- Integrated Framework for Probing Multimodal Protein Foundation Models with Structure-Functional Interpretability Analysis in Detection of Allosteric Binding Sites.bioRxiv : the preprint server for biology · 2026Article
199 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
backgroundLigand binding site prediction from protein structure has many applications related to elucidation of protein function and structure based drug discovery. It often represents only one step of many in complex computational drug design efforts. Although many methods have been published to date, only few of them are suitable for use in automated pipelines or for processing large datasets. These use cases require stability and speed, which disqualifies many of the recently introduced tools that are either template based or available only as web servers.
resultsWe present P2Rank, a stand-alone template-free tool for prediction of ligand binding sites based on machine learning. It is based on prediction of ligandability of local chemical neighbourhoods that are centered on points placed on the solvent accessible surface of a protein. We show that P2Rank outperforms several existing tools, which include two widely used stand-alone tools (Fpocket, SiteHound), a comprehensive consensus based tool (MetaPocket 2.0), and a recent deep learning based method (DeepSite). P2Rank belongs to the fastest available tools (requires under 1 s for prediction on one protein), with additional advantage of multi-threaded implementation.
conclusionsP2Rank is a new open source software package for ligand binding site prediction from protein structure. It is available as a user-friendly stand-alone command line program and a Java library. P2Rank has a lightweight installation and does not depend on other bioinformatics tools or large structural or sequence databases. Thanks to its speed and ability to make fully automated predictions, it is particularly well suited for processing large datasets or as a component of scalable structural bioinformatics pipelines.
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