linjc16 / r2-ddi

[BIB 2023] Official implementation of "R2-DDI: Relation-aware Feature Refinement for Drug-drug Interaction Prediction".

R²-DDI: Relation-aware Feature Refinement for Drug-drug Interaction Prediction

This repository is the official implementation of R²-DDI: Relation-aware Feature Refinement for Drug-drug Interaction Prediction. The code is originally forked from Fairseq and DVMP.

Requirements and Installation

• PyTorch version == 1.8.0
• PyTorch Geometric version == 1.6.3
• RDKit version == 2020.09.5

You can build the Dockerfile or use the docker image teslazhu/pretrainmol36:latest.

To install the code from source, you should first install rdkit,

conda install -y -c conda-forge rdkit=2020.09.5

Then, install other dependencies, like

git clone https://github.com/linjc16/R2-DDI.git

pip install fairseq
pip uninstall -y fairseq 

pip install ninja
python setup.py build_ext --inplace

Getting Started

Dataset

The drugbank dataset can be seen in dataset folder. The twosides dataset can be download here.

Data Preprocessing

We evaluate our models on DrugBank and TwoSides benchmark sets. ddi_zoo/scripts/data_process and ddi_zoo/scripts/twosides/data_process are folders for preprocessing of DrugBank and TwoSides, respectively. To generate the binary data for fairseq, take the transductive setting for DrugBank as an example, run

python ddi_zoo/scripts/data_process/split_trans.py

python ddi_zoo/scripts/data_process/run_process_trans.py

bash ddi_zoo/scripts/data_process/run_binarize_trans.sh

Note that you need to change the file paths accordingly.

Training and Test

All traning and test scripts can be seen in ddi_zoo/scripts. For instance,

bash ddi_zoo/scripts/train_trans/run_gcn_feat_int_cons.sh new_build1 0.01 1e-4 256

bash ddi_zoo/scripts/train_trans/inf_gcn_feat_int_cons.sh new_build1 0.01 1e-4 256

Contact

Please feel free to submit a Github issue if you have any questions or find any bugs. We do not guarantee any support, but will do our best if we can help.