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  <a href="https://www.biorxiv.org/content/10.1101/2024.08.01.606258"><img src="https://img.shields.io/badge/Paper-bioRxiv-green" style="max-width: 100%;"></a> <a href="http://www.denovo-pinal.com/"><img src="https://img.shields.io/badge/Pinal-red?label=Server" style="max-width: 100%;"></a> <a href="https://huggingface.co/westlake-repl/Pinal"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-yellow?label=Model" style="max-width: 100%;"></a>
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  Pinal is an advanced protein design framework that translates human design intent into novel protein sequences. It utilizes a two-stage process: first generating protein structures from language instructions, then designing sequences based on those structures. With a substantial parameter count of 16 billion and trained on a diverse dataset comprising 1.7 billion protein-text pairs, Pinal demonstrates marked improvements in both performance and generalization capabilities for novel protein structures.
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- ![scale](C:\Users\PC\Desktop\Graph\scale.png)
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  For more information, please refer to our [preprint](https://www.biorxiv.org/content/10.1101/2024.08.01.606258).
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  ## Usage
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- Please refer to the README.md in the [Github repository](https://github.com/westlake-repl/Denovo-Pinal) for details on how to use the model.
 
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  <a href="https://www.biorxiv.org/content/10.1101/2024.08.01.606258"><img src="https://img.shields.io/badge/Paper-bioRxiv-green" style="max-width: 100%;"></a> <a href="http://www.denovo-pinal.com/"><img src="https://img.shields.io/badge/Pinal-red?label=Server" style="max-width: 100%;"></a> <a href="https://huggingface.co/westlake-repl/Pinal"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-yellow?label=Model" style="max-width: 100%;"></a>
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  Pinal is an advanced protein design framework that translates human design intent into novel protein sequences. It utilizes a two-stage process: first generating protein structures from language instructions, then designing sequences based on those structures. With a substantial parameter count of 16 billion and trained on a diverse dataset comprising 1.7 billion protein-text pairs, Pinal demonstrates marked improvements in both performance and generalization capabilities for novel protein structures.
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/665feb99ea2cc04a641db7f6/hZaZu7-GbAN-LShTNDV3c.png)
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  For more information, please refer to our [preprint](https://www.biorxiv.org/content/10.1101/2024.08.01.606258).
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  ## Usage
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+ Please refer to the README.md in the [Github repository](https://github.com/westlake-repl/Denovo-Pinal) for details on how to use the model.