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.
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.
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.
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.
