ESMFold2: The Bitter Lesson Is Coming for Proteins
Original: 🔬ESMFold2: The Bitter Lesson is Coming for Proteins - Alex Rives, BioHub
Biohub’s ESMFold2 pushes open protein structure prediction and design toward scalable biological world models.
Latent Space interviews Biohub’s Alex Rives about ESMFold2 and the broader ESM protein modeling stack. The discussion centers on datasets versus inductive bias, and whether protein biology is entering its own Bitter Lesson era. The key implication is that large-scale evolutionary sequence data and open models may become foundations for structure prediction, interaction modeling, and programmable biology.
This Latent Space piece features Biohub's Alex Rives and discusses ESMFold2 along with the significance of the ESM family of models for protein AI. The "Bitter Lesson" in the title refers to a recurring experience in the AI field: over the long run, general methods that can ingest more data and compute tend to outperform methods that rely heavily on hand-crafted human inductive biases. Applied to protein research, the question becomes: how much biochemical knowledge should we encode into a model's architecture, and how much should we leave for large-scale evolutionary sequence data and scalable models to learn on their own?
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