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https://github.com/thinng/GraphDTA
GraphDTA: Predicting drug-target binding affinity with graph neural networks
https://github.com/thinng/GraphDTA
Last synced: about 2 months ago
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GraphDTA: Predicting drug-target binding affinity with graph neural networks
- Host: GitHub
- URL: https://github.com/thinng/GraphDTA
- Owner: thinng
- Created: 2019-06-11T23:56:36.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-04-13T05:13:35.000Z (over 3 years ago)
- Last Synced: 2024-02-13T10:09:42.242Z (7 months ago)
- Language: Python
- Homepage: https://doi.org/10.1093/bioinformatics/btaa921
- Size: 67.4 MB
- Stars: 189
- Watchers: 4
- Forks: 75
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# 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.