ESMFold2, using only a single input sequence, achieves near-parity with AlphaFold3 on general protein-protein complex prediction, performing within 3 points on the DocQ pass rate metric.

The previous generation of protein models, ESM2, hit a performance plateau with increasing parameters, whereas the new ESM Cambrian model continues to improve without a plateau.

Using sparse autoencoders, researchers found that the latent space of protein language models decomposes into interpretable features corresponding to biological concepts like amino acids, structural motifs, and protein domains.