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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 ,the data and trained models.

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.

Using

1.mol_featurizer.py generates input for TransformerCPI model.

2.main.py trains TransformerCPI model.

Author

Lifan Chen

Mingyue Zheng

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, Volume 36, Issue 16, 15 August 2020, Pages 4406–4414, https://doi.org/10.1093/bioinformatics/btaa524

TransformerCPI2.0

TransformerCPI2.0 is now available at https://github.com/lifanchen-simm/transfomerCPI2.0 !