https://github.com/lirongwu/psc-cpi
Code for AAAI 2024 paper "PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction"
https://github.com/lirongwu/psc-cpi
compound-protein-interaction contrastive-learning protein-modeling protein-representation-learning
Last synced: about 1 month ago
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Code for AAAI 2024 paper "PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction"
- Host: GitHub
- URL: https://github.com/lirongwu/psc-cpi
- Owner: LirongWu
- License: mit
- Created: 2023-12-14T03:56:37.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-08T14:53:36.000Z (3 months ago)
- Last Synced: 2025-03-27T07:48:01.891Z (about 2 months ago)
- Topics: compound-protein-interaction, contrastive-learning, protein-modeling, protein-representation-learning
- Language: Python
- Homepage:
- Size: 141 KB
- Stars: 26
- Watchers: 1
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Multi-Scale Protein Sequence-Structure Contrasting for Compound-Protein Interaction Prediction (PSC-CPI)
This is a PyTorch implementation of Protein Sequence-sructure Contrasting for CPI Prediction (PSC-CPI), and the code includes the following modules:
* Dataset loader (train/val/test)
* Four evaluation settings: Seen-Both, Unseen-Compound, Unseen-Protein, and Unseen-Both
* Four evaluation metrics: CPI pattern prediction (AUPRC and AUROC) and CPI Strength Prediction (RMSE and PPCs)
* Pre-training, fine-tuning, and inference paradigm
## Main Requirements
* numpy==1.21.6
* scipy==1.7.3
* torch==1.6.0
* sklearn == 1.0.2
## Dataset
The datasets used in this paper are available in:
https://drive.google.com/file/d/1_iZ8B1JZkCKmKlQNewOCr3kbnWfAIc-r/view?usp=sharing
## Description
* train.py
* Pre-training, fine-tuning, and inference
* models.py
* ProteinEmbed_Model() - Learning protein sequence and structure representations
* prot_data_aug() -- Data augmentation on proteins
* loss_inter() -- loss for CPI pattern prediction
* loss_affn() -- loss for CPI strength prediction
* loss_contras() -- loss for (pre-training) multi-scale contrastive learning
* dataloader.py
* data_loader() -- Load train, val, and test data (with four evaluation data spilts)
* utils.py
* set_seed() -- Set radom seeds for reproducible results
* cal_affinity_torch() -- Use Pytorch to calculate CPI affinity (RMSE and PPCs)
* cal_interaction_torch() -- Use Pytorch to calculate CPI pattern (AUPRC and AUROC)
## Running the code
1. Install the required dependency packages
3. To pre-train and fine-tune the model, please run with proper hyperparameters:
```
python train.py --task_mode 0 --modality seq_str_linear --pre-train 1 --seq_encoder HRNN --str_encoder GAT
```
where (1) *task_mode* is one of the two CPI tasks: 0 (Strength Prediction) and 1 (Pattern Prediction); (2) *modality* is one of the three inference settings: 'seq_str_linear' (both two modalities are provided), 'sequence' (only sequence is provided), and 'structure' (only structure is provided); (3) *pre-train* denotes whether the pre-training is conducted: 0 (w/o pre-training) and 1 (w/ pre-training); (4) *seq_encoder* is one of the four protein sequence encoders: HRNN, LSTM, bi-LSTM, and Transformer; and (5) *str_encoder* is one of the three protein structure encoders: GCN, GAT, and SAGE.
## Citation
If you find this project useful for your research, please use the following BibTeX entry.
```
@inproceedings{wu2024psc,
title={Psc-cpi: Multi-scale protein sequence-structure contrasting for efficient and generalizable compound-protein interaction prediction},
author={Wu, Lirong and Huang, Yufei and Tan, Cheng and Gao, Zhangyang and Hu, Bozhen and Lin, Haitao and Liu, Zicheng and Li, Stan Z},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={38},
number={1},
pages={310--319},
year={2024}
}
```