--- a
+++ b/README.md
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+# Resources:
+
++ 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
+  These file were downloaded from https://github.com/hkmztrk/DeepDTA/tree/master/data
+
+###  Source codes:
++ 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 GraphDTA model.
++ models/ginconv.py, gat.py, gat_gcn.py, and gcn.py: proposed models GINConvNet, GATNet, GAT_GCN, and GCNNet receiving graphs as input for drugs.
+
+# Step-by-step running:
+
+## 0. Install Python libraries needed
++ Install pytorch_geometric following instruction at https://github.com/rusty1s/pytorch_geometric
++ Install rdkit: conda install -y -c conda-forge rdkit
++ Or run the following commands to install both pytorch_geometric and rdkit:
+```
+conda create -n geometric python=3
+conda activate geometric
+conda install -y -c conda-forge rdkit
+conda install pytorch torchvision cudatoolkit -c pytorch
+pip install torch-scatter==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
+pip install torch-sparse==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
+pip install torch-cluster==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
+pip install torch-spline-conv==latest+cu101 -f https://pytorch-geometric.com/whl/torch-1.4.0.html
+pip install torch-geometric
+
+```
+
+## 1. Create data in pytorch format
+Running
+```sh
+conda activate geometric
+python create_data.py
+```
+This returns kiba_train.csv, kiba_test.csv, davis_train.csv, and davis_test.csv, saved in data/ folder. These files are in turn input to create data in pytorch format,
+stored at data/processed/, consisting of  kiba_train.pt, kiba_test.pt, davis_train.pt, and davis_test.pt.
+
+## 2. Train a prediction model
+To train a model using training data. The model is chosen if it gains the best MSE for testing data.  
+Running 
+
+```sh
+conda activate geometric
+python training.py 0 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, respectively;
+ and the third argument is for the index of the cuda, 0/1 for 'cuda:0' or 'cuda:1', respectively. 
+ Note that your actual CUDA name may vary from these, so please change the following code accordingly:
+```sh
+cuda_name = "cuda:0"
+if len(sys.argv)>3:
+    cuda_name = "cuda:" + str(int(sys.argv[3])) 
+```
+
+This returns the model and result files for the modelling achieving the best MSE for testing data throughout the training.
+For example, it returns two files model_GATNet_davis.model and result_GATNet_davis.csv when running GATNet on Davis data.
+
+## 3. Train a prediction model with validation 
+
+In "3. Train a prediction model", a model is trained on training data and chosen when it gains the best MSE for testing data.
+This follows how a model was chosen in https://github.com/hkmztrk/DeepDTA. The result by two ways of training is comparable though.
+
+In this section, a model is trained on 80% of training data and chosen if it gains the best MSE for validation data, 
+which is 20% of training data. Then the model is used to predict affinity for testing data.
+
+Same arguments as in "3. Train a prediction model" are used. E.g., running 
+
+```sh
+python training_validation.py 0 0 0
+```
+
+This returns the model achieving the best MSE for validation data throughout the training and performance results of the model on testing data.
+For example, it returns two files model_GATNet_davis.model and result_GATNet_davis.csv when running GATNet on Davis data.