TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments

This repository contains the source code and the data.

TransformerCPI

Setup and dependencies

Dependencies:

• python 3.6
• pytorch >= 1.2.0
• numpy
• RDkit = 2019.03.3.0
• pandas
• Gensim >=3.4.0

Data sets

The data sets with train/test splits are provided as .7z file in a directory called 'data'.

The test set is created specially for label reversal experiments.

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Using

1.mol_featurizer.py generates input for TransformerCPI model.

2.main.py trains TransformerCPI model.

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Author

Lifan Chen ([email protected])

Mingyue Zheng（[email protected])

Citation

Lifan Chen, Xiaoqin Tan, Dingyan Wang, Feisheng Zhong, Xiaohong Liu, Tianbiao Yang, Xiaomin Luo, Kaixian Chen, Hualiang Jiang, Mingyue Zheng, TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments, Bioinformatics, , btaa524, https://doi.org/10.1093/bioinformatics/btaa524