Deep learning identifies synergistic drug combinations for treating COVID-19

This is the implementation of our PNAS 2021 paper: https://www.pnas.org/content/118/39/e2105070118

Dependencies

Our model is tested in Linux with the following packages:

• CUDA >= 11.1
• PyTorch == 1.8.2 (LTS Version)
• Numpy >= 1.18.1
• tqdm

Data

The covid combination data is stored in the data/covid folder.

• data/covid/dti.csv is the drug-target interaction data
• data/covid/single_agent.csv is the single-agent antiviral activity data
• data/covid/synergy_train.csv is the drug combination synergy data (training set)
• data/covid/synergy_test.csv is the drug combination synergy data (test set)
• data/covid/synergy_test.csv is the drug combination synergy data (test set under "compounds out" strategy)
• data/covid/synergy_experiment.csv contains the top 30 drug combinations ranked by ComboNet and we experimentally tested them in a VeroE6 CPE assay.

Model training

To run our model under five-fold cross-validation, please run

python covid_train.py --save_dir ckpts/combonet --num_folds 5

Model inference

To use ComboNet to predict synergy for new drug combinations, please run

python predict.py --checkpoint_dir ckpts/combonet --test_path data/covid/synergy_experiment.csv

If there are multiple model checkpoints in checkpoint_dir, the above script will combine them as a model ensemble. The output score will be an average of scores from each model. Compounds with higher scores are more likely to be synergistic.