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Predicts the 3D shape of proteins together with DNA, RNA, small molecules and antibodies, the starting point for much modern drug design.
AlphaFold 3, from Google DeepMind and Isomorphic Labs, predicts the three-dimensional structure of proteins together with DNA, RNA, small molecules, ions and antibodies by combining a Pairformer trunk with a diffusion module that generates all atom positions jointly. Described in Nature 2024, it extends AlphaFold 2 from single proteins to complexes and ligands, which is why it has become the starting point for much modern structure-based drug design, including work on cancer targets. The weights were released for academic use late in 2024, while Isomorphic uses successor models commercially. Its predictions are static structures, and accuracy for antibody-antigen complexes is still limited, so experimental validation remains essential for binder design. For a newcomer: AlphaFold 3 draws a picture of how a drug or antibody might fit onto its target before anyone makes it.
AlphaFold 3 combines a Pairformer with a diffusion module over all biomolecular types.
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.
The shape of nearly every protein is now available to any researcher in seconds instead of years, which shortens the path from a cancer target to a designed molecule. It does not by itself produce drugs: binding pockets, dynamics and cellular context still need experiment.
Query for this technology: (TITLE:"AlphaFold 3" OR ABSTRACT:"AlphaFold 3") AND (cancer OR tumor OR tumour OR oncology OR carcinoma OR lymphoma OR leukemia OR leukaemia OR myeloma OR sarcoma OR melanoma OR glioma). Results are unfiltered search hits about AlphaFold 3, not a curated reading list.
Shares Drug discovery roadmap: screening in mice → maps of dependency → designing in silico, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow, AI-driven drug & target discovery and the tags foundation-model, structure.
Shares the tags foundation-model, structure.
Shares Drug discovery roadmap: screening in mice → maps of dependency → designing in silico, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow, AI-driven drug & target discovery and the tag foundation-model.
Shares Google DeepMind (and Google Research), AI in oncology roadmap: pattern readers → foundation models → agents in the workflow and the tag foundation-model.
Shares Drug discovery roadmap: screening in mice → maps of dependency → designing in silico, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow, AI-driven drug & target discovery and the tag foundation-model.
Shares Drug discovery roadmap: screening in mice → maps of dependency → designing in silico, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow, AI-driven drug & target discovery and the tag foundation-model.
Shares Google DeepMind (and Google Research) and the tag foundation-model.
Shares Drug discovery roadmap: screening in mice → maps of dependency → designing in silico, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow, AI-driven drug & target discovery and the tag foundation-model.
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DeepMind's model of proteins with ligands, nucleic acids and modifications; code is released for non-commercial use and weights by request.