PyDTINet is a Python implementation of DTINet, a network integration approach for drug-target interaction prediction. This Python implementation is provided for the convenience of users who want to use DTINet with Python in their own research. For the original implementation (written in MATLAB) associated with the publication that was used to generate the results in the paper, please see the original DTINet repo. More details about the algorithm can be found in our Nature Communications paper.

PyDTINet currently only supports Python 2 due to the dependency on the IMC library used to perform the matrix completion step (we welcome contributions to support Python 3! See feature request below). You can create a conda environment with the required dependencies by running the following command:

conda env create -n dtinet -f environment.yml
conda activate dtinet

1. Install the IMC library.

    cd lib
    bash install_imc.sh
    cd ..
   

2. Unzip the data files.

    unzip data.zip
   

3. Run a quick demo of the DTINet algorithm.

    cd example
    python demo.py
   

   Due to a random split of the train/test data, the results may vary slightly from the results produced by the original MATLAB implementation. However, the results should be similar enough to demonstrate the basic functionality of the algorithm.

If you would like to contribute to this project by porting the code to Python 3, please feel free to submit a pull request. We will be happy to review it and merge it into the master branch. The major change would be adapting the file that implements the Python bindings for the IMC library to work with Python 3 (see lib/leml-imf/src/python/train_mf.cpp after unzipping the leml-imf-src.zip file).

Luo, Y., Zhao, X., Zhou, J., Yang, J., Zhang, Y., Kuang, W., Peng, J., Chen, L. & Zeng, J. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nature Communications 8, (2017).

@article{Luo2017,
  author = {Yunan Luo and Xinbin Zhao and Jingtian Zhou and Jinglin Yang and Yanqing Zhang and Wenhua Kuang and Jian Peng and Ligong Chen and Jianyang Zeng},
  title = {A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information},
  doi = {10.1038/s41467-017-00680-8},
  url = {https://doi.org/10.1038/s41467-017-00680-8},
  year  = {2017},
  month = {sep},
  publisher = {Springer Nature},
  volume = {8},
  number = {1},
  journal = {Nature Communications}
}

Please submit GitHub issues or contact Yunan Luo (luoyunan[at]gmail[dot]com) for any questions related to the source code.