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: This is the first substantial demonstration that competing pharmaceutical companies can use federated learning to turn otherwise siloed experimental structures into a materially stronger shared drug-discovery model; the private data roughly tripled the drug-relevant training set. The result points to data access—not only architecture—as a major remaining bottleneck for protein–drug modeling, but it is consortium-reported, not yet peer-reviewed, and neither the private data nor the trained weights are public.
Technical results and official announcement · Consortium overview · OpenFold3 technical report · OpenFold3 code · Independent analysis (Nature)