Dòng tin

RT by @ylecun: 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.

RT by @ylecun: A newly released AI tool has generated an atlas of more than one billion predicted protein structures and billions more protein sequences. https://go.nature.com/4fblM0Z