1. What happened
Federated fine-tuning through Apheris; source data remains in each contributor's controlled environment.
2. Buyer / seller
OpenFold Consortium, AlQuraishi Lab, Apheris is the AI buyer or model-development side. AbbVie, Johnson & Johnson, Bristol Myers Squibb, Takeda, Astex Pharmaceuticals owns or contributes the proprietary corpus.
3. Economics
No data-specific consideration disclosed. Economics scope: not disclosed. Strategic Signal has not estimated undisclosed consideration.
4. Proprietary data
Several thousand experimentally determined, proprietary protein–small molecule structures contributed by pharmaceutical companies for privacy-preserving fine-tuning of OpenFold3. Historical depth: not_disclosed. Refreshability: periodic consortium contributions.
5. Rights and restrictions
Data stays in each owner's environment; secure aggregation prevents raw-data pooling. Ownership: no disclosed transfer. Training: yes explicit. Post-training: yes explicit fine tuning.
6. Why the data matters for AI
Post-train and validate OpenFold3 for joint protein–ligand structure prediction and drug discovery. High: experimentally validated molecular structures encode expensive real-world outcomes.
7. Asset Score — Pending/100
Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values. Score coverage: 0%.
| Factor | Weight | Rating / 5 | Points |
|---|---|---|---|
| Uniqueness / scarcity | 12 | Unknown | — |
| Historical depth | 8 | Unknown | — |
| Decision → outcome richness | 13 | Unknown | — |
| Decision Value Density | 13 | Unknown | — |
| Real-world grounding | 10 | Unknown | — |
| Domain economic value | 9 | Unknown | — |
| Refreshability | 8 | Unknown | — |
| Proprietary advantage | 9 | Unknown | — |
| Model-learning usefulness | 10 | Unknown | — |
| Non-replicability | 4 | Unknown | — |
| Rights usability | 4 | Unknown | — |
8. Transaction Signal Score — Pending/100
Legacy score withdrawn pending evidence-linked scoring and human approval. The superseded v1.0.0 release retains the original values. Score coverage: 0%.
| Factor | Weight | Rating / 5 | Points |
|---|---|---|---|
| Disclosed economics | 16 | Unknown | — |
| Clean price discovery | 16 | Unknown | — |
| Separability of data consideration | 15 | Unknown | — |
| Explicit training / model-improvement use | 14 | Unknown | — |
| Clarity of rights purchased | 12 | Unknown | — |
| Competing bids | 10 | Unknown | — |
| Strategic buyer quality | 6 | Unknown | — |
| Repeatability / precedent value | 6 | Unknown | — |
| Independent model-uplift evidence | 5 | Unknown | — |
9. M&A / valuation implications
Demonstrates that federated learning can monetize embedded scientific archives without separating or selling the underlying intellectual property.
10. Evidence boundary
The existence and federated training purpose are disclosed facts. Strategic-value and M&A statements are Strategic Signal analysis.