mallertian/NewGS

Predicting drug-target affinity based on recurrent neural networks and graph convolutional neural networks

NewGS

Predicting drug-target affinity based on recurrent neural networks and graph convolutional neural networks

Source

• README.md: this file.
• data/davis/folds/test_fold_setting1.txt,train_fold_setting1.txt; data/davis/Y,ligands_can.txt,proteins.txt data/kiba/folds/test_fold_setting1.txt,train_fold_setting1.txt; data/kiba/Y,ligands_can.txt,proteins.txt Dataset
• embed : Dictionaries
• create_data.py: create data in pytorch format
• utils.py: include TestbedDataset used by create_data.py to create data, and performance measures.
• training.py: train a model.

Running

Install Python libraries needed

Create a conda virtual environment and intall libs

• Install pytorch
• Install pytorch_geometric following instruction at https://github.com/rusty1s/pytorch_geometric
• Install rdkit: conda install -y -c conda-forge rdkit
• Install tensorflow : pip install tensorflow-gpu==1.15
• Install keras: pip install keras ==2.3.1
• Install transformers: pip install transformers

Create data files

python create_data.py 

It will create four .pt files in the data/processed folder

training

python training.py 0 0

where the first argument is for the index of the datasets, 0/1 for 'davis' or 'kiba', respectively; the second argument is for the index of the models, 0/1/2/3 for GINConvNet, GATNet, GAT_GCN, or GCNNet. This returns result.csv, containing the performance of the proposed models on the two datasets. The measures include rmse, mse, pearson, spearman, and ci.