ArticleeLife2023
How accurately can one predict drug binding modes using AlphaFold models?
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 1 of them a synthesis that pooled it.
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Who cites it
74 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Article
- Proteome-Wide Target Identification Using Reactive Metallo-Scaffolds (r-mS): A Platform for Metallodrug Discovery.Angewandte Chemie (International ed. in English) · 2026Article
- Evaluating molecular docking for binding affinity predictions: a systematic analysis of key parameters and the utility of AlphaFold2 structures for the Schrödinger dataset.Journal of computer-aided molecular design · 2026Article
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- DeorphaNN: Virtual screening of GPCR peptide agonists using AlphaFold-predicted active-state complexes and deep learning embeddings.Molecular cell · 2026Article
- Bitter taste TAS2R14 and TAS2R46 receptors bound to G proteins: comparison of cryo-EM, AlphaFold, and molecular dynamics structures.European biophysics journal : EBJ · 2026Article
- How Medically Important Antimicrobials Bind to the 30S Ribosomal Subunit in a Bacterial Pathogen.Biochemistry · 2026Article
- Deep generative models for 3D structure-based drug design and molecular optimisation: a comprehensive survey.Journal of computer-aided molecular design · 2026Review
- Target identification and assessment in the era of AI.Nature reviews. Drug discovery · 2026Review
- Stabilizing Plasmodium falciparum proteins for small molecule drug discovery.Protein science : a publication of the Protein Society · 2026Article
- Computer-aided structural modeling and drug discovery for G-protein-coupled receptors in the age of artificial intelligence.Current opinion in structural biology · 2026Review
- Beyond the canonical: The role of post-transcriptional regulation in drug-target interaction prediction.PLoS computational biology · 2026Article
- Improving AlphaFold2 Performance in Virtual Screens Targeting GPCRs by Enhancing Binding-Site Conformational Sampling.Journal of chemical information and modeling · 2026Article
- Reimagining pharmacology education.Pharmacological reviews · 2026Review
- Article
- AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network.Nature methods · 2026Article
- Discovery of Covalent Ligands with AlphaFold3.Journal of the American Chemical Society · 2026Article
- Unfreezing structural biology for drug discovery.Nature chemical biology · 2026Review
- Systematic evaluation of computational tools to predict the effects of mutations on protein-ligand binding affinity in the absence of experimental structures.Briefings in bioinformatics · 2026Article
14 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
Abstract
Computational prediction of protein structure has been pursued intensely for decades, motivated largely by the goal of using structural models for drug discovery. Recently developed machine-learning methods such as AlphaFold 2 (AF2) have dramatically improved protein structure prediction, with reported accuracy approaching that of experimentally determined structures. To what extent do these advances translate to an ability to predict more accurately how drugs and drug candidates bind to their target proteins? Here, we carefully examine the utility of AF2 protein structure models for predicting binding poses of drug-like molecules at the largest class of drug targets, the G-protein-coupled receptors. We find that AF2 models capture binding pocket structures much more accurately than traditional homology models, with errors nearly as small as differences between structures of the same protein determined experimentally with different ligands bound. Strikingly, however, the accuracy of ligand-binding poses predicted by computational docking to AF2 models is not significantly higher than when docking to traditional homology models and is much lower than when docking to structures determined experimentally without these ligands bound. These results have important implications for all those who might use predicted protein structures for drug discovery.
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