ArticleNature communications2024
Structure prediction of protein-ligand complexes from sequence information with Umol.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Efficient Discovery of Potent AKR1B10 Inhibitors Using Co-folding Model Boltz-2.Journal of chemical information and modeling · 2026Article
- DockM8: an all-in-one open-source platform for consensus virtual screening in drug design.Journal of cheminformatics · 2026Article
- A curated benchmark for cofolding models on kinase conformational states.npj drug discovery · 2026Article
- Integrative AI-assisted modeling suggests CPPF binding at a composite α/β-tubulin interface pocket dominated by β-tubulin contacts.PLoS computational biology · 2026Article
- AlphaDTA: integrating AlphaFold3 embeddings and 3D complex structures for drug-target binding affinity prediction.Journal of cheminformatics · 2026Article
- The use of artificial intelligence (AI) in neuropsychiatric drug discovery: current challenges and future directions.Translational psychiatry · 2026Review
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Open-Source Molecular Docking and AI-Augmented Structure-Based Drug Design: Current Workflows, Challenges, and Opportunities.International journal of molecular sciences · 2026Review
- Measuring and locating the changes in protein structure using MELO.Nature communications · 2026Article
- Advanced Computational Methods in Protein Simulations: A Case Study of Enhanced Sampling Applied to Membrane Transporters.Advances in experimental medicine and biology · 2026Review
- Assessing the potential of deep learning for protein-ligand docking.Nature machine intelligence · 2026Article
- Generalizable compound protein interaction prediction with a model incorporating protein structure aware and compound property aware language model representations.Communications chemistry · 2025Article
- Advancing active compound discovery for novel drug targets: insights from AI-driven approaches.Acta pharmacologica Sinica · 2025Review
- Integrating machine learning and protein-ligand interaction profiling for the discovery of METTL3 inhibitors.Scientific reports · 2025Article
- Investigating whether deep learning models for co-folding learn the physics of protein-ligand interactions.Nature communications · 2025Article
- Beyond rigid docking: deep learning approaches for fully flexible protein-ligand interactions.Briefings in bioinformatics · 2025Review
- Article
- Quantification of the Impact of Structure Quality on Predicted Binding Free Energy Accuracy.Journal of chemical information and modeling · 2025Article
- Assessing interaction recovery of predicted protein-ligand poses.Journal of cheminformatics · 2025Article
- EnGCI: enhancing GPCR-compound interaction prediction via large molecular models and KAN network.BMC biology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Protein-ligand docking is an established tool in drug discovery and development to narrow down potential therapeutics for experimental testing. However, a high-quality protein structure is required and often the protein is treated as fully or partially rigid. Here we develop an AI system that can predict the fully flexible all-atom structure of protein-ligand complexes directly from sequence information. We find that classical docking methods are still superior, but depend upon having crystal structures of the target protein. In addition to predicting flexible all-atom structures, predicted confidence metrics (plDDT) can be used to select accurate predictions as well as to distinguish between strong and weak binders. The advances presented here suggest that the goal of AI-based drug discovery is one step closer, but there is still a way to go to grasp the complexity of protein-ligand interactions fully. Umol is available at: https://github.com/patrickbryant1/Umol .
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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.