Join us for a webinar exploring how federated learning is transforming drug discovery by enabling models to train on sensitive data across organizations – without the data ever leaving each party's secure environment.

Tech Talk 1: Federated Confidential Computing with NVIDIA FLARE
Speaker: Chester Chen, Senior Product Manager & Engineering Manager, NVIDIA
Abstract: In this lightning talk, I will introduce the on-prem Federated Confidential Computing.

Tech Talk 2: What Federated Training Delivered: Co-Folding and ADMET Results from Apheris Pharma Networks
Speaker: Robin Röhm is the CEO and Co-Founder of Apheris | Ian Hales is Senior Director of AI Engineering at Apheris
Abstract: Apheris builds superior drug discovery models through federated data, powered by NVIDIA FLARE. In this talk, we go through our co-folding and ADMET results. For co-folding, we show how we designed the benchmarks for the federated model and how OpenFold3 was trained on five pharma companies’ proprietary structural data, inside their security boundaries, in under ten weeks. For ADMET, we show how the network is set up and how it learns across pharma partners’ private measurements. We also cover how the resulting models compare with public baselines, how purpose-built data generation extends the networks, and how Apheris Foundry lets teams run, customize, and integrate the models in their own environment.

Tech Talk 3: Federated Learning Across Chemical Space: Lessons from Lilly TuneLab, Eli Lilly & Company
Speaker: Sam Holo, PhD, Data Scientist on the TuneLab technical team at Eli Lilly & Company
Abstract: In this talk, we will describe how TuneLab addresses these challenges through deliberate choices: from underlying model architecture to federated training and evaluation strategy. We will share insights from deploying these approaches across a growing network of partners and discuss what we have learned about making federated learning practical at scale in real-world drug discovery settings.

Venue:
virtual, join from anywhere.

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