Implementation of the GVP-Transformer, which was used in the paper "Learning inverse folding from millions of predicted structures" for de novo protein design alongside Alphafold2

Last update: May 06, 2022

Overview

GVP Transformer (wip)

Implementation of the GVP-Transformer, which was used in the paper Learning inverse folding from millions of predicted structures for de novo protein design alongside Alphafold2. The base neural network consists of an invariant geometric network, GVP, combined with an encoder / decoder transformer.

It will also offer the tools for enabling autoregressive infilling

Citations

@article {Hsu2022.04.10.487779,
    author  = {Hsu, Chloe and Verkuil, Robert and Liu, Jason and Lin, Zeming and Hie, Brian and Sercu, Tom and Lerer, Adam and Rives, Alexander},
    title   = {Learning inverse folding from millions of predicted structures},
    elocation-id = {2022.04.10.487779},
    year    = {2022},
    doi     = {10.1101/2022.04.10.487779},
    publisher = {Cold Spring Harbor Laboratory},
    URL     = {https://www.biorxiv.org/content/early/2022/04/10/2022.04.10.487779},
    eprint  = {https://www.biorxiv.org/content/early/2022/04/10/2022.04.10.487779.full.pdf},
    journal = {bioRxiv}
}

Owner

Phil Wang

Working with Attention. It's all we need

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