ArticleNature methods2026
AF2BIND: predicting small-molecule binding sites using the pair representation of AlphaFold2.
Article in Nature methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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Who cites it
12 citing papers in PubMed.
- ESpma: A method for assessing biological/non-biological interfaces using point-clouds-based structural features and protein language models.Protein science : a publication of the Protein Society · 2026Article
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- Explainable AI reveals the allosteric blind spot in protein-ligand binding predictions.Cell reports. Physical science · 2026Article
- DeorphaNN: Virtual screening of GPCR peptide agonists using AlphaFold-predicted active-state complexes and deep learning embeddings.Molecular cell · 2026Article
- From physics-based intelligence to molecular design: linking mechanistic learning across scales.MedScience · 2026Article
- CAFE: A Co-folding Approach for Fragment Exploration of Allosteric and Cryptic Binding Sites.bioRxiv : the preprint server for biology · 2026Article
- PDBe-SIFTS: an open-source tool for Structure Integration with Function, Taxonomy, and Sequences, featuring improved alignment, scoring scheme, and accelerated search.bioRxiv : the preprint server for biology · 2026Article
- Training bias and sequence alignments shape protein-peptide docking by AlphaFold and related methods.Protein science : a publication of the Protein Society · 2025Article
- Modeling protein conformational ensembles by guiding AlphaFold2 with Double Electron Electron Resonance (DEER) distance distributions.Nature communications · 2025Article
- Article
- Applicability of AlphaFold2 in the modeling of dimeric, trimeric, and tetrameric coiled-coil domains.Protein science : a publication of the Protein Society · 2025Article
- Sparks of function by de novo protein design.Nature biotechnology · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Identification of small-molecule binding sites in proteins is an important task for drug discovery. Despite previous homology- and machine-learning-based approaches to this problem, true de novo binding-site prediction remains a challenge. Here we use features from a pretrained neural network to train a logistic regression model, AF2BIND, for accurate prediction of de novo binding sites. AF2BIND identifies binding sites without relying on homology modeling, multiple sequence alignments or knowledge of a pocket-compatible ligand. Interpretable aspects of the model can be used to predict chemical properties of compatible ligands. We apply AF2BIND on the human proteome to produce a database that includes thousands of unseen binding sites in disease-relevant proteins. We anticipate AF2BIND will be used to focus drug discovery efforts and uncover functional sites in proteins across the tree of life.
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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.