A deep learning ensemble approach to prioritize antiviral drugs against novel coronavirus SARS-CoV-2 for COVID-19 drug repurposing

Abstract

The alarming pandemic situation of Coronavirus infectious disease COVID-19, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has become a critical threat to public health. The unexpected outbreak and unrealistic progression of COVID-19 have generated an utmost need to realize promising therapeutic strategies to fight the pandemic. Drug repurposing-an efficient drug discovery technique from approved drugs is an emerging tactic to face the immediate global challenge. It​ offers a time-efficient and cost-effective way to find potential therapeutic agents for the disease. Artificial Intelligence-empowered deep learning models enable the rapid identification of potentially repurposable drug candidates against diseases. This study presents a deep learning ensemble model to prioritize clinically validated anti-viral drugs for their potential efficacy against SARS-CoV-2. The method integrates the similarities of drug chemical structures and virus genome sequences to generate feature vectors. The best combination of features is retrieved by the convolutional neural network in a deep learning manner. The extracted deep features are classified by the extreme gradient boosting classifier to infer potential virus-drug associations. The method could achieve an AUC of 0.8897 with 0.8571 prediction accuracy and 0.8394 sensitivity under the fivefold cross-validation. The experimental results and case studies demonstrate the suggested deep learning ensemble system yields competitive results compared with the state-of-the-art approaches. The top-ranked drugs are released for further wet-lab researches.

Keywords: Antiviral drugs; COVID-19 drug repurposing; Convolutional neural network; Deep learning; SARS-coV-2; XGBoost.

Fig. 1

The workflow of DLEVDA to prioritize potential virus–drug associations. With the integrated features of viruses and drugs as input, DLEVDA comprises two essential components: CNN-based feature learning and XGBoost-based classification.

Fig. 2

The basic architecture of the CNN-XGBoost model.

Fig. 3

ROC and PR curves obtained by DLEVDA under the fivefold CV experiment.

Fig. 4

Comparison of DLEVDA with the competing classifiers under the fivefold CV experiment.

Fig. 5

ROC and PR curves obtained by DLEVDA on a different drug–virus association dataset under the fivefold CV experiment.