Accurate structure prediction of biomolecular interactions with AlphaFold 3
Paper cited by one technology page and one roadmap page, indexed on Europe PMC as PubMed record 38718835 and published in Nature; the citing pages link this DOI, which is how the record was matched.
Overview
The introduction of AlphaFold 2 1 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design 2-6. Here we describe our AlphaFold 3 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-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.2.3 7,8. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.
Indexed on Europe PMC as PubMed record 38718835 (DOI 10.1038/s41586-024-07487-w). Matched by DOI alone: one technology page and one roadmap page cite this DOI among their external links (the pages are listed under Related), and this page was written so that the citation resolves inside OnCo. No figure has been checked by an editor.
One technology page and one roadmap page on OnCo cite this paper by its DOI; this record gives the citation a page of its own so a reader can follow it without leaving OnCo. Read the abstract above alongside the citing pages listed under Related; the record was created automatically from the Europe PMC entry and its figures have not been checked by hand.
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Similar pages
not linked directly; found by shared links- Key paperAlphaFold 2: predicting protein structures to near-experimental accuracy
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