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README.md

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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:
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**ProteinInvBench: Benchmarking Protein Design on Diverse Tasks, Models, and Metrics**
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[**ProteinInvBench: Benchmarking Protein Design on Diverse Tasks, Models, and Metrics**](https://openreview.net/pdf?id=bqXduvuW5E)
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[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).
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## Introduction
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[ProteinInvBench]() is the first comprehensive benchmark for protein design. The main contributions of our paper could be listed as four points below:
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[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:
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* **Tasks:** We extend recent impressive models from single-chain protein design to the scenarios of multi-chain and de-novoprotein design.
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* **Models:** We integrate recent impressive models into a unified framework for efficiently reproducing and extending them to custom tasks.
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## Citation
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```
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@article{gao2023knowledge,
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title={Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinement},
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author={Gao, Zhangyang and Tan, Cheng and Li, Stan Z},
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journal={arXiv preprint arXiv:2305.15151},
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year={2023}
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@inproceedings{
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gao2023proteininvbench,
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title={ProteinInvBench: Benchmarking Protein Inverse Folding on Diverse Tasks, Models, and Metrics},
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author={Zhangyang Gao and Cheng Tan and Yijie Zhang and Xingran Chen and Lirong Wu and Stan Z. Li},
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booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
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year={2023},
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url={https://openreview.net/forum?id=bqXduvuW5E}
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}
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```
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