FEDERATED NETWORK
AI Structural Biology (AISB) Network
An industry-led collaboration of nine leading pharmaceutical companies, formed to advance AI for drug discovery.
FEDERATED NETWORK
An industry-led collaboration of nine leading pharmaceutical companies, formed to advance AI for drug discovery.
WHY THE NETWORK EXISTS
One major challenge in advancing AI for drug discovery is the limited availability of protein and ligand structural data suitable for training models that perform in industrial settings. Public databases are commonly acknowledged as insufficient for the precision required in drug discovery portfolios.
Few public structures contain a drug
Co-folding models are trained on public structural data, above all the Protein Data Bank (PDB), the main public archive of experimentally determined protein structures. It holds more than 240,000 of them. Only about 10,600 contain an approved or investigational drug, and only about 3,000 of those were added after AlphaFold3’s training cutoff.
Reduced accuracy on drug-relevant complexes
Public benchmarks look solved: AlphaFold3 places more than 75% of ligands within 2 Å on PoseBusters, and Boltz-2, Protenix and Chai-1 report comparable results. Accuracy drops on what drug discovery depends on: antibody–antigen complexes, and drug-like ligands on targets that public data barely covers.
Relevant data exists but cannot be shared
Pharmaceutical companies have accumulated large proprietary datasets of protein–ligand complexes over decades of research. This data remains siloed due to intellectual property, confidentiality and regulatory constraints.
Sources: RCSB Protein Data Bank holdings, 2026; Abramson et al., Nature, 2024; Buttenschoen et al., Chemical Science, 2024 (PoseBusters); Boltz-2, Protenix and Chai-1 technical reports.
PROVEN IN THE FIRST INITIATIVE
In the Federated OpenFold3 Initiative, five member companies jointly fine-tuned OpenFold3 in under ten weeks, in collaboration with the AlQuraishi Lab at Columbia University, and without sharing any underlying data. The resulting model outperforms every public model tested, and any model fine-tuned on a single partner’s data alone.
Five member companies contributed proprietary structures.
AbbVie, Astex Pharmaceuticals, Bristol Myers Squibb, Johnson & Johnson and Takeda each fine-tuned OpenFold3 locally, inside their own environment. Only model parameters were aggregated. No structures were pooled.
20,000+
private structures from active drug-discovery programs, roughly tripling the drug-relevant data available for training
+11 pts
lead over the strongest public model tested, on both accuracy metrics
<10 weeks
from first federated training run to a state-of-the-art model
Ahead of every public model tested, on both measures.
+10 pts pose accuracy
+11 pts interface quality
Axes: fraction of structures with PL-lDDT ≥ 0.8 and fraction with bisyRMSD ≤ 2 Å, on 1,056 held-out private structures from five pharma partners, ranked selection. AISB-1-Fed also outperformed every model fine-tuned on a single partner’s data alone.
IN THE PRESS
AlphaFold is running out of data — so drug firms are building their own version
The AI model OpenFold3 takes a crucial step in making protein predictions
OpenFold3 debuts as Apache-licensed protein co-folding model, with Apheris bringing it into pharma IT
HOW THE NETWORK WORKS
The Network operates through initiatives: collaborations among members on a specific use case, across both small and large molecules.
Participation is per initiative.
Each initiative brings together the members whose scientific priorities it matches. Members who take part receive the resulting model.
Models can come from anywhere.
Initiatives can build on models from academic labs, private model providers or the open-source community.
WHY MEMBERS CONTRIBUTE
Data never leaves.
Training runs inside each member’s own environment. Only privacy-preserving model updates are aggregated into the global model.
Custodians control computation.
Data custodians define access policies governing which computations may be trained or evaluated on their data.
Models are attack-tested.
ML-attack-based validation confirms that trained models do not leak sensitive information or permit reverse engineering of proprietary data.
Members own the models.
Background and foreground IP are separated by contract. The weights of the model an initiative produces belong to the participating members.
THE ROLE OF APHERIS
Neutral by design.
Apheris provides the federated technology, streamlines the scientific discussions and alignment across pharma companies, the project planning, and the contracts and approvals.
INITIATIVES
The first initiative is complete. What follows is selected on scientific significance and strategic value to members.
DELIVERED
The Federated OpenFold3 Initiative
Five member companies jointly fine-tuned OpenFold3 across more than 20,000 proprietary structures, run with the AlQuraishi Lab at Columbia University. Protein–protein interface quality improved significantly as a by-product, without being optimized for.
IN SCOPE
More initiatives to come
The Network is scoping further initiatives across binding, off-target effects and downstream prediction. The longer-term direction is set out in our collaborative vision below.
OPEN
Bring your own priorities
If the problem blocking your programs is not on the roadmap yet, it can be. Initiatives are shaped around the scientific priorities of the members taking part.
OUR COLLABORATIVE VISION
Towards in-silico precision approaching X-ray crystallography.
The AISB Network uses secure federated learning to protect both data privacy and model performance, with the long-term goal of accelerating AI in molecule design by reaching a precision in predicted protein complex structures that approaches X-ray crystallography.
Reaching that precision depends on four things:
Data available for training while preserving confidentiality and IP
Infrastructure for federated learning and high-performance compute
Leading-edge AI algorithms
Partnership between pharma and technology
AI-powered drug discovery changes materially once data from multiple pharmaceutical companies can be used jointly to train the models that in-silico discovery depends on.
Get the AISB Network overview
Download the detailed overview for how the Network is structured and what participating in an initiative involves.
FAQ
Frequently asked questions about AISB.
JOIN OUR NETWORK
Join the AI Structural Biology Network
Members take part in the initiatives that match their scientific priorities and receive the models those initiatives produce. We will come back to you to arrange an introductory conversation.
