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--- |
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license: mit |
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tags: |
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- biology |
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- protein |
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--- |
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# ESM-MSA1 |
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For more information, please see the ESM team's [Github repo](https://github.com/facebookresearch/esm) for more information. |
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## Citations |
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If you find the models useful in your research, we ask that you cite the relevant paper: |
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```bibtex |
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@article{rives2019biological, |
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author={Rives, Alexander and Meier, Joshua and Sercu, Tom and Goyal, Siddharth and Lin, Zeming and Liu, Jason and Guo, Demi and Ott, Myle and Zitnick, C. Lawrence and Ma, Jerry and Fergus, Rob}, |
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title={Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences}, |
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year={2019}, |
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doi={10.1101/622803}, |
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url={https://www.biorxiv.org/content/10.1101/622803v4}, |
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journal={PNAS} |
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} |
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``` |
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For the self-attention contact prediction: |
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```bibtex |
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@article{rao2020transformer, |
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author = {Rao, Roshan M and Meier, Joshua and Sercu, Tom and Ovchinnikov, Sergey and Rives, Alexander}, |
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title={Transformer protein language models are unsupervised structure learners}, |
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year={2020}, |
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doi={10.1101/2020.12.15.422761}, |
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url={https://www.biorxiv.org/content/10.1101/2020.12.15.422761v1}, |
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journal={bioRxiv} |
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} |
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``` |
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For the MSA Transformer: |
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```bibtex |
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@article{rao2021msa, |
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author = {Rao, Roshan and Liu, Jason and Verkuil, Robert and Meier, Joshua and Canny, John F. and Abbeel, Pieter and Sercu, Tom and Rives, Alexander}, |
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title={MSA Transformer}, |
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year={2021}, |
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doi={10.1101/2021.02.12.430858}, |
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url={https://www.biorxiv.org/content/10.1101/2021.02.12.430858v1}, |
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journal={bioRxiv} |
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} |
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``` |
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