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Here we describe our AlphaFold\u20093 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein\u2013ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein\u2013nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody\u2013antigen prediction accuracy compared with AlphaFold-Multimer v.2.3\n                    <jats:sup>7,8<\/jats:sup>\n                    . Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.\n                  <\/jats:p>","DOI":"10.1038\/s41586-024-07487-w","type":"journal-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T12:02:58Z","timestamp":1715169778000},"page":"493-500","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18099,"title":["Accurate structure prediction of biomolecular interactions with AlphaFold 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