Took me a while to figure out what all the ESMFold2 rage was about. At first, the benchmarking data didn't look super remarkable to me but i…

Took me a while to figure out what all the ESMFold2 rage was about. At first, the benchmarking data didn't look super remarkable to me but it turns there are many impressive aspects: - Fully open source, open weights + massive ESM Atlas (1.1B structures vs 0.2B for AF3). - SOTA performance despite no MSA use. MSA search and triangular attention were simply taken out of the base model. - Direct consequence, super low latency inference: 1024-residue protein structure prediction in 9 secs, still outperforming prior models on antibody-antigen tasks. - Best in class PPI and antibody-antigen results. 65% pass rate on antibody-antigen benchmarks after inference-time scaling, significant improvement over AF3. - Tons of experimental data, in particular with lab-validated miniprotein binders plus single-chain antibodies across 5 targets in cancer and immunology. Binding affinities consistent with therapeutic activity. - Inference-time scaling benefits PPI: Multiple seeds + selection by confidence show real gains on challenging antibody-antigen predictions, leading to comments/hypotheses that it has learned an energy-function-like behavior via the folding module. - Base model works without MSAs, but providing them further boosts prediction quality on difficult protein-protein interaction cases. One caveat: No true scoring for protein-protein interactions, making it harder to assess which specific residues or domains are reliably involved in binding. Today we're announcing ESMFold2, an open scientific engine to power prediction, design, and discovery across protein biology. The new model delivers state of the art performance on protein interactions, especially antibodies, a critical modality for therapeutics. We have designed…

Source: Yann LeCun (X) | 2026-05-29