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Join experts from Rhino Federated Computing, J&J and more No images? Click here NEW WEBINARTuesday, March 24, 11:30 AM ETRecent advances in protein structure prediction have transformed early drug discovery, but they have also revealed a new bottleneck: understanding what a molecule looks like is no longer the primary challenge—understanding whether it will bind with sufficient strength and selectivity to matter biologically is. Binding affinity prediction depends on diverse experimental evidence. Critical output from screening assays, biophysical measurements, simulations, and negative results are distributed across companies and collaborators and cannot realistically be centralized due to intellectual property, governance, and regulatory constraints. Consequently, progress is increasingly limited, not by model design alone, but by how models can safely learn from fragmented data. In this session, we will discuss how federated AI approaches enable organizations to contribute to, improve and evaluate affinity models without sharing proprietary datasets. By allowing models to train and run where data already resides, institutions can contribute signal while maintaining control over sensitive information. We will explore:
The broader question we will examine is whether the next phase of AI-enabled discovery will depend less on isolated model breakthroughs and more on new mechanisms for secure, collective learning across the scientific ecosystem.
ANDREA BORTOLATO VP, DRUG DISCOVERY
ELKE NELSON-NICHOLS VP, LIFE SCIENCES
PETE DIMAGGIO DIRECTOR
BENJAMIN PLACKETT SCIENCE JOURNALIST Designed by
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