Five drugmakers show federated training can sharply improve AI protein–drug predictions without sharing raw data
AbbVie, Astex, Bristol Myers Squibb, Johnson & Johnson, and Takeda jointly fine-tuned OpenFold3 Preview 2 across 20,167 private protein–ligand structures while keeping every structure inside its owner’s environment. On 1,056 held-out structures, the resulting AISB-1-Fed model raised high-quality interface predictions from 35.6% to 52.1% and correct ligand poses from 28.9% to 46.8%, outperforming both the public OpenFold3 checkpoint and Boltz-2 on the consortium’s private evaluation.
Why it made the cut: The five-company experiment provides substantial evidence that otherwise siloed experimental structures can improve a shared drug-discovery model without pooling raw data. Announced September 14, it falls within this briefing’s 48-hour lookback. The evaluation uses held-out projects from the same participating companies, not a fully external test, and does not establish binding-affinity accuracy or general superiority across chemistry; the findings are consortium-reported, and the private data and trained weights are not public.
Technical results and official announcement · Consortium overview · OpenFold3 technical report · OpenFold3 code · Independent analysis (Nature)
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