🧬 What are protein language models (PLMs) actually learning about biology? Our paper introduces InterPLM - a framework that reveals interpretable features in PLMs using sparse autoencoders, giving us a window into how these models represent protein structure and function. 🧵(1/8)
www.biorxiv.org/content/10.1... InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders Code: github.com/ElanaPearl/I... Interactive site: interplm.ai Nice work by Elana Simon from James Zou lab
InterPLM: Discovering Interpretable Features in Protein Language Models via Sparse Autoencoders
Protein language models (PLMs) have demonstrated remarkable success in protein modeling and design, yet their internal mechanisms for predicting structure and function remain poorly understood. Here w...
biorxiv.org
