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forrest-yijie-zhang authored Nov 15, 2023
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This repository is an open-source project for benchmarking structure-based protein design methods, which provides a variety of collated datasets, reprouduced methods, novel evaluation metrics, and fine-tuned models that are all integrated into one unified framework. It also contains the implementation code for the paper:

**ProteinInvBench: Benchmarking Protein Design on Diverse Tasks, Models, and Metrics**
[**ProteinInvBench: Benchmarking Protein Design on Diverse Tasks, Models, and Metrics**](https://openreview.net/pdf?id=bqXduvuW5E)

[Zhangyang Gao](https://scholar.google.com/citations?user=4SclT-QAAAAJ&hl=en), [Cheng Tan](https://chengtan9907.github.io/), [Yijie Zhang](https://scholar.google.com/citations?user=Q9Gby5wAAAAJ&hl=en), [Xingran Chen](https://www.chenxingran.com/), [Stan Z. Li](https://scholar.google.com/citations?user=Y-nyLGIAAAAJ&hl).

## Introduction

[ProteinInvBench]() is the first comprehensive benchmark for protein design. The main contributions of our paper could be listed as four points below:
[ProteinInvBench](https://openreview.net/pdf?id=bqXduvuW5E) is the first comprehensive benchmark for protein design. The main contributions of our paper could be listed as four points below:

* **Tasks:** We extend recent impressive models from single-chain protein design to the scenarios of multi-chain and de-novoprotein design.
* **Models:** We integrate recent impressive models into a unified framework for efficiently reproducing and extending them to custom tasks.
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## Citation

```
@article{gao2023knowledge,
title={Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinement},
author={Gao, Zhangyang and Tan, Cheng and Li, Stan Z},
journal={arXiv preprint arXiv:2305.15151},
year={2023}
@inproceedings{
gao2023proteininvbench,
title={ProteinInvBench: Benchmarking Protein Inverse Folding on Diverse Tasks, Models, and Metrics},
author={Zhangyang Gao and Cheng Tan and Yijie Zhang and Xingran Chen and Lirong Wu and Stan Z. Li},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2023},
url={https://openreview.net/forum?id=bqXduvuW5E}
}
```

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