Five rival drugmakers trained one AI without sharing their data, and it beat the open model

Drug companies guard their molecular data as closely as anything they own, which is exactly why AI for drug discovery has been held back — the best training data is locked inside rival firms that will never pool it. Five of them just found a way around that without giving anything up.

AbbVie, Astex Pharmaceuticals, Bristol Myers Squibb, Johnson & Johnson and Takeda, working with a lab at Columbia University, jointly trained an AI model called AISB-1-Fed on more than 20,000 of their proprietary molecular structures. No structure file left any company along the way.

The Result

The payoff was measurable. On a held-out test of over 1,000 private structures, the jointly trained model produced high-quality predictions of how proteins and drug molecules fit together for 52.1 per cent of them — up from 35.6 per cent for the public model it was built on, and ahead of another leading system.

That is a large jump in a task that sits at the heart of designing new drugs: predicting how a candidate molecule will bind to its target. Better predictions mean fewer dead ends in the lab.

The Trick: Federated Learning

The method is the reason this could happen at all, and it is worth understanding.

Normally, training one AI on five companies' data would mean putting that data in one place — a non-starter for competitors guarding trade secrets. Federated learning avoids that. Each company trains a copy of the model inside its own systems, on its own private data, and only the resulting model improvements — not the data — are combined. The shared model learns from everyone's structures without anyone seeing anyone else's.

The companies did it in under ten weeks, and, crucially, no confidential structure ever left its owner. Rivals collaborated without trusting each other with the crown jewels.

Why This Matters Beyond Pharma

The significance runs wider than drug discovery, and this is the part worth drawing out.

A vast amount of the world's most valuable data is locked away precisely because it is sensitive or competitive — patient records, financial data, industrial measurements. The standard assumption is that you cannot build good AI on it without first pooling it, which is often legally or commercially impossible. Federated learning is a working counter-example: a demonstration that competitors, or privacy-bound institutions, can jointly build a better model than any could alone, without surrendering the underlying data.

If that generalises, it changes what AI can be built in exactly the domains where data is most guarded.

The Honest Caveats

Some restraint is warranted. This is a reported result on an internal benchmark, not yet the independently scrutinised, peer-reviewed literature, and the companies involved have an interest in it looking good. A better score on a binding-prediction benchmark is a meaningful step, not a delivered drug — the distance from a good prediction to an approved medicine is long and mostly not about AI. And federated learning is harder to run than ordinary training, which is part of why it is not already everywhere.

But the core claim — competitors improved a shared model without sharing data — is the kind of thing that is checkable, and the collaboration itself is real.

What To Watch

Whether the result holds up under independent and peer review. Whether more industries with locked-away data adopt federated learning to build models they otherwise could not. And whether better binding predictions actually shorten real drug-discovery timelines, which is the outcome that would justify the effort.