InstructProtein

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InstructProtein

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Code and data for the ACL2024 paper "InstructProtein: Aligning Human and Protein Language via Knowledge Instruction".

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// repository documentation

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# InstructProtein: Aligning Human and Protein Language via Knowledge Instruction
This repository is the official model and benchmark proposed in a paper: [InstructProtein: Aligning Human and Protein Language via Knowledge Instruction](https://aclanthology.org/2024.acl-long.62/). The model is available at [HuggingFace](https://huggingface.co/hicai-zju/InstructProtein).

## Description

InstructProtein is the first large generative language model exploring the feasibility of bidirectional generation between human and protein language. The idea comes from the research of the [Understudied Proteins Initiative](https://www.nature.com/articles/s41587-022-01316-z): Some proteins, such as the tumor suppressor p53, have been studied extensively. By contrast, thousands of human proteins remain 'understudied'. To align proteins with natural language, InstructProtein is fine-tuned on a high-quality dataset constructed from Knowledge Instruction.

## Installation
```
>> git clone https://github.com/HICAI-ZJU/InstructProtein
>> cd InstructProtein
```

Edit `configuration.py` script with the absolute location of `dump` folder in your environment.

### How to evaluate models
We provide scripts for evaluating the protein understanding and design capabilities of large language models in `./benchmarks`. Let’s take subcellular localization prediction as an example.

```
>> cd benchmarks/scripts
>> python run_subcellular_localization.py
```

### How to train models
We provide a toy instruction dataset in `./dump/pretrain/raw/instructions.txt`.

```
# Build training dataset
>> cd ./scripts
>> bash ./create_training_dataset.sh

# Train model
>> bash ./training.sh
```

## Limitations

The current model, developed through instruction tuning using the knowledge instruction dataset, serves as a preliminary example. Despite its initial success in controlled environments, it lacks the robustness to manage complex, real-world, production-level tasks.

## Reference

If you use our repository, please cite the following related paper:

```
@inproceedings{wang-etal-2024-instructprotein,
    title = "{I}nstruct{P}rotein: Aligning Human and Protein Language via Knowledge Instruction",
    author = "Wang, Zeyuan  and
      Zhang, Qiang  and
      Ding, Keyan  and
      Qin, Ming  and
      Zhuang, Xiang  and
      Li, Xiaotong  and
      Chen, Huajun",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.62",
    pages = "1114--1136",
}
```

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