IHS-H2-Bioinformatics-Drug-Target

This project is for the IEEE Covid-19 data hackathon's H2: Bioinformatics Drug Challenge.

Our final method is an feed-forward neural network model to predict docking scores of candidate drug molecules on SARS-CoV-2 protein targets. We also tried XGBoost regressor and other , but their performance in mean absolute error(MAE) is higher than our NN model.

In the repository, src file contains all of our codes and BestNN_circ file contains our pretrained NN models.

In src:

• FineTuneXGB.ipynb: finetune 3 main parameters of XGBoost regressor(baseline) on each protein target including the number of the estimators, the depth of the tree and the minimum child weight.
• FingerprintsGeneration.ipynb : generate 4 types of fingerprints(MACCS fingerprint, RDKit fingerprint, ECFP4 and MHFP6) and get their UMAP visualization.
• NN_RandomSearch.ipynb: apply random search for tuning our feed-forward neural network's hyper-parameters including layer depth, layer width and dropout rate.
• NN_arch.pk: saved best neural network architecture from random search.
• TestPredcition.ipynb: use our pretrained model to get docking score prediction on test set.

In BestNN_circ:

• bestNN_circ_col{x}: saved neural network model pretrained on training set for the protein target in column x (x ranges from 0 to 17). The model use the architecture in src/NN_arch.pk.

For more details, please contact [email protected] & [email protected] .