SA-DDI

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# Learning size-adaptive molecular substructures for explainable drug–drug interaction prediction by substructure-aware graph neural network
## Note
If you want to use the code in the cold start scenario, you should add dropout to MLP layers and use weight decay.

## Requirements

numpy==1.18.1 \
tqdm==4.42.1 \
pandas==1.0.1 \
rdkit==2009.Q1-1 \
scikit_learn==1.0.2 \
torch==1.11.0 \
torch_geometric==2.0.4 \
torch_scatter==2.0.9

## Step-by-step running:  
### 1. DrugBank
- First, cd SA-DDI/drugbank, and run data_preprocessing.py using  
  `python data_preprocessing.py -d drugbank -o all`  
  Running data_preprocessing.py convert the raw data into graph format. \
   Create a directory using \
  `mkdir save`  
- Second, run train.py using 
  `python train.py --fold 0 --save_model`

to train SA-DDI. The training record can be found in save/ folder.

Explanation of parameters

- --n_iter: number of iterations
  - --fold: {0, 1, 2}
  - --epochs: number of epochs
  - --weight_decay: weight decay
  - --batch_size: batch size
  - --save_model: whether save the model or not, for example, 'python train.py' will not save the model and 'python train.py --save_model' will save the model.
  - --lr: learning rate
### 2. TWOSIDES
- First, cd SA-DDI/drugbank, and run data_preprocessing.py using  
  `python data_preprocessing.py -d twosides -o all`   
  Running data_preprocessing.py convert the raw data into graph format.
  Create a directory using \
  `mkdir save`
- Second, run train.py using 
  `python train.py --fold 0 --save_model`

to train SA-DDI. The training record can be found in save/ folder.

SA-DDI

SA-DDI Latest Version Download

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