Protein AI: Alex Rives Scaled Data, Not Just Models
Biohub's Alex Rives explains how his ESMC protein AI leveraged billions of metagenomic sequences to build a protein "world model," showing the power of scaling data over human intuition.
40 hours of podcasts, in 5 minutes.
This episode features Alex Rives from Biohub discussing the application of the 'bitter lesson' (scaling laws and empirical evidence) to protein biology through the ESMC language model. Rives explains how massive datasets, particularly metagenomics, enabled ESMC to build a comprehensive 'world model' of proteins, facilitating the design of novel therapeutics like antibodies and offering deep insights into protein function through mechanistic interpretability. The discussion also covers Biohub's ambitious Virtual Biology Initiative, aiming to integrate advanced experimental techniques with AI to create predictive digital representations of biological systems, from molecules to cells, to accelerate scientific discovery and combat disease.
Biohub's Alex Rives explains how his ESMC protein AI leveraged billions of metagenomic sequences to build a protein "world model," showing the power of scaling data over human intuition.
Biohub's Alex Rives reveals how ESMC protein AI, using Sparse Autoencoders, autonomously discovered deep biological principles like the 'nucleophilic elbow' from sequence data alone.