
ProLLM
[COLM'24] We propose Protein Chain of Thought (ProCoT), which replicates the biological mechanism of signaling pathways as language prompts. It considers a signaling pathway as a protein reasoning process, which starts from upstream proteins and passes through several intermediate proteins to transmit biological signals to downstream proteins.
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ProLLM is a framework that leverages Large Language Models to interpret and analyze protein sequences and interactions through natural language processing. It introduces the Protein Chain of Thought (ProCoT) method to transform complex protein interaction data into intuitive prompts, enhancing predictive accuracy by incorporating protein-specific embeddings and fine-tuning on domain-specific datasets.
README:
This repo presents the implementation of the ProLLM🧬
The paper has been accepted by COLM 2024.
Paper link: https://openreview.net/forum?id=2nTzomzjjb#discussion
Arxiv link: https://arxiv.org/abs/2405.06649
Website link: https://tiuxuxsh76075.github.io/prollm.github.io/
We present Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction, abbreviated as ProLLM. This innovative framework leverages the advanced capabilities of Large Language Models (LLMs) to interpret and analyze protein sequences and interactions through a natural language processing lens.
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Protein Chain of Thought (ProCoT) Method: ProLLM introduces the Protein Chain of Thought (ProCoT) method, transforming the complex, structured data of protein interactions into intuitive, natural language prompts.
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Enhanced Predictive Accuracy: This approach not only facilitates a deeper understanding of protein functions and interactions but also enhances the model's predictive accuracy by incorporating protein-specific embeddings and instruction fine-tuning on domain-specific datasets.
pip install -r requirements.txt
- Download SHS27K, SHS148K, STRING and Human
- Preprocess the dataset into Protein Chain of Thought (ProCoT) by the ./data_preprocess/[dataset_name]/preprocess.py
python [dataset]_preprocess.py
- Do the embedding replacement in ./embedding_replacement (Embedding needs to be run on your own, as it is too large to be uploaded.)
- Instruction fintuning in ./Instruction_fintuning
- Do training through train_ProLLM.py , make sure the location of model and tokenizer in right place. Feel free to change 'num_epochs', 'batch_size' and 'learning_rate'.
python train_ProLLM.py --model_dir /path/to/model/dir --tokenizer_dir /path/to/tokenizer/dir --data_file dataset.csv --num_epochs 1 --batch_size 2 --learning_rate 3e-4 --output_dir /path/to/output/dir
If you find this paper helpful, please consider to cite:
@inproceedings{
jin2024prollm,
title={Pro{LLM}: Protein Chain-of-Thoughts Enhanced {LLM} for Protein-Protein Interaction Prediction},
author={Mingyu Jin and Haochen Xue and Zhenting Wang and Boming Kang and Ruosong Ye and Kaixiong Zhou and Mengnan Du and Yongfeng Zhang},
booktitle={First Conference on Language Modeling},
year={2024},
url={https://openreview.net/forum?id=2nTzomzjjb}
}
@article{jin2024prollm,
title={ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction},
author={Jin, Mingyu and Xue, Haochen and Wang, Zhenting and Kang, Boming and Ye, Ruosong and Zhou, Kaixiong and Du, Mengnan and Zhang, Yongfeng},
journal={arXiv e-prints},
pages={arXiv--2405},
year={2024}
}
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