How Federated Learning Could Bridge Pharma's Data Divide

Srijit Seal explains why many AI models for toxicology and bioactivity remain highly program‑ and company‑specific. Each organization explores a unique region of chemical space shaped by its own historical decisions, while public datasets often lack critical negative results. This absence of standardized, high‑quality data limits the transferability of global models, pushing pharma to rely on local Quantitative Structure–Activity Relationship (QSAR) approaches. Seal highlights federated learning as a promising path forward, enabling companies to learn from private datasets without exposing proprietary data—extending some of these benefits to smaller biotechs.