NHGNN-DTA

NHGNN-DTA: A Node-adaptive Hybrid Graph Neural Network for Interpretable Drug-target Binding Affinity Prediction

File list

• Data：It contains the data files for training.
• Vocab: It contains the vocab files for protein and drug tokenizer.
• Code: It contains code files.

Generation of contact map

Generating contact map relies on the following libraries:

• hhsuite (https://github.com/soedinglab/hh-suite)
• ccmpred (https://github.com/soedinglab/CCMpred)

Then you can run scripts.py to get the corresponding npy files.

Run code

Use the main_pretraining.py in the Code folder to pre-train model.

You can customize the parameters by modifying the file, including the save path of the dataset, training super parameters and model weights.

python main_pretraining.py

After pre-training, the main.py file is used for training. And the path of model parameter file needs to be setting.

python main.py

Cold drug/target/drug-target split

Use the split.py in the Code folder to split dataset in cold setting

The name of the dataset can be set to "davis" or "kiba" by "dataset_name".

The random seed can be set by "SEED"

python split.py --dataset_name davis --SEED 42

Then you will get the training, validation and test data sets of the three cold start settings corresponding to the data set.

Requirement

numpy~=1.20.3 rdkit~=2021.09.2 networkx~=2.6.3 pandas~=1.3.4 torch~=1.12.1 scikit-learn~=0.24.2 scipy~=1.7.1 tqdm~=4.62.3