GraphDTI

Description

GraphDTI is a deep learning framework to predict the drug-target interactions using the integrated four types of features, which contain the information of drugs, namely Mol2vec; the information proteins, namely ProtVec; the binding sites of proteins, namely Bionoi-AE; and the information of the drug perturbed local protein-protein interaction(PPI) network of target proteins, namely Graph2vec. Based on the local PPI network, we provided a method to optimize the graph2vec features. The details of feature generation and optimization can be found in https://github.com/Guannan1900/GraphDTI_preprocess. The deep neural network(DNN) is implemented based on Pytorch. The pipeline of GraphDTI is shown below:

Files

• permu_feature_importance.json: The indices and important scores of all the integrated features. We provide a feature selection processdure in order to mitigate the overfitting problem. The details can be found in https://github.com/Guannan1900/GraphDTI_preprocess/tree/master/feature_selection.
• train_validation_list.csv: The information of split protocols for cross-validation. We design two types of protocols for cross-validation in order to evaluate the generalizability of the model. The details can be found in https://github.com/Guannan1900/GraphDTI_preprocess/tree/master/clustering.
• training_label.pickle: The labels for all instances in the training dataset.
• test_list.pkl and test_label.pickle: The names and labels for all instances in the test dataset.

"Note: the training and test dataset can be available at: https://osf.io/ugvd9/"

Usage

The framework is provided the training and prediction models

1. The training model

• Split the training and validation data of two protocols for cross-validation
  • required file: train_validation_list.csv
  • input: the training data path, training_data/
  • output: the validation name list in tow fold of two protocols for cross-validation, input_list_cluster/ and input_list_random/

python train_validation_list_generation.py -inpath 'training_data/'

• Train the multilayer perceptron(MLP) for cross validation
  • required files:
    • permu_feature_importance.json
    • input_list_cluster/ or input_list_random/
    • training_label.pickle
  • input:
    • -data_dir: the training data path
    • -bs: batch size, recommended value is 32
    • -lr: learning rate, recommended value is 0.0001
    • -epoch: number of epoch for taining, recommended value is 30
    • -protocol: two protocols for cross-validation, which are 'cluster' or 'random'
  • output:
    • -output: location for the trained models to be saved
    • -logs: location for the logs to be saved

python train.py -data_dir 'training_data/' -protocol 'cluster'

2. The prediction model

• Using pre-trained model for prediction on a test dataset
  • required files:
    • permu_feature_importance.json
    • test_list.pkl
    • test_label.pickle
  • input:
    • -data_dir: a test dataset path
    • -model: the pre-trained model for GraphDTI
  • output:
    • -output: The location for the roc results on a test dataset

python predict.py -data_dir 'test_data/' -model 'GraphDTI.pt'

Prerequisites

1. Python 3.7 or higher
2. Pytorch 1.2.0 or higher
3. Numpy 1.17.3
4. CUDA if you would like to run on GPU(s)