Federated techniques such as federated learning and federated analysis have emerged as a powerful paradigm for enabling multi-center research on sensitive clinical data while preserving patient privacy. In this study we provide a federated leraning framework for ML research.
Five pharma companies fine-tuned OpenFold3 Preview 2 across 20,167 private structures, none of which left their environments. On 1,056 held-out structures, high-quality interface predictions rose from 35.6% to 52.1% and correct ligand poses from 28.9% to 46.8%, ahead of every public model. A first for federated drug discovery.
GEN published a thoughtful coverage of last month's Bio-IT World plenary keynote where JT from Apheris presented a session on federated learning and fine-tuning for drug discovery.
ADMET liabilities drive 40–45% of clinical attrition, and models break on novel chemistry which is precisely where chemists need them most. Pre-competitive federated networks address this through complementary data collaboration. A walk through the evidence behind the Apheris ADMET Network.
NEWSLETTER
A quaterly briefing on operationalizing and customizing co-folding, ADMET and antibody developability models. Written by Robin Röhm, co-founder and CEO at Apheris.
