Idris

Today we’re releasing the fastest? protein folding models ever! @biohub actually trained ESMFold2 variants from the 300M and 600M parameter ESMC backbones, however, they did not release the models with confidence heads. Synthyra fixed that, now, the FastPLM versions of ESMFold2-300 and 600 can return pLDDT, pTM, and iPTM alongside structure. These models are actually very strong. On FoldBench, moving from 300M to 600M only gains about 0.008 IDDT and 1.8 percentage points in PPI pass rates. Moving from 600M to 6B only gains another 0.028 IDDT and 6 points. Our new confidence heads showcase high correlation to truth on held out evaluation sets. Roughly 0.8-0.9 spearman correlation across LDDT, TM, and DockQ. They can even tell which residues are intrinsically disordered from pLDDT alone with ROC AUC ~0.86. Importantly, correlation to the 6B model is ~0.7 on pLDDT, ~0.8 for pTM, and ~0.7 for iPTM. There are many gradient-based protein design methods at this point, (Bindcraft, Mosaic, etc.) so this monotonic relationship between these tiny model’s outputs and the production ESMFold2 are super important for cheap gradient based design, where critic stages can still come from the larger model. We have also integrated @AnthropicAI's new optimizations for protein language models, including our ESMFold2 implementations, for even more speed! Of course, like any FastPLM model, these are super easy to use – even on consumer hardware. I predicted the barnase-barstar complex structure in 18 seconds on my modest 3070 GPU, including imports and model download. Check out FastPLMs and try ESMFold2-300 and 600 today! github.com/Synthyra/FastPLMs huggingface.co/Synthyra/ESMF… huggingface.co/Synthyra/ESMF…