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ntranoslab/vesm_scores

Proteome-wide VESM variant effect scores This repository provides precomputed proteome-wide (UniProtKB, hg19, and hg38) variant-effect prediction scores using the latest VESM models developed in the paper "Compressing the collective knowledge of ESM into a single protein language model" by Tuan Dinh, Seon-Kyeong Jang, Noah Zaitlen and Vasilis Ntranos. Models: VESM_3B, VESM3, sequence-only VESM3, and VESM++ (available at https://huggingface.co/ntranoslab/vesm). VESM_3B and… See the full description on the dataset page: https://huggingface.co/datasets/ntranoslab/vesm_scores.

sourceHugging Faceupdated 1y agoView on Hugging Face

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Proteome-wide VESM variant effect scores

This repository provides precomputed proteome-wide (UniProtKB, hg19, and hg38) variant-effect prediction scores using the latest VESM models developed in the paper "Compressing the collective knowledge of ESM into a single protein language model" by Tuan Dinh, Seon-Kyeong Jang, Noah Zaitlen and Vasilis Ntranos.

—Models: VESM_3B, VESM3, sequence-only VESM3, and VESM++ (available at https://huggingface.co/ntranoslab/vesm).

VESM_3B and VESM3 are individual protein language models based on ESM2 (3B) and ESM3. The sequence-only VESM3 is the version of VESM3 using only sequence as the input. VESM++ is the ensemble of VESM_3B and VESM3. ```


Please see the corresponding GitHub repo (https://github.com/ntranoslab/vesm) for more details.

## License  <a name="license"></a>
The predictions of VESM_3B are distributed under the MIT License. 
The VESM3 and VESM++ models are built with ESM3-Open (EvolutionaryScale), which is available under a [non-commercial license agreement](https://www.evolutionaryscale.ai/policies/cambrian-open-license-agreement).