COVID-19 drug repurposing model based on pigeon-inspired optimizer and rough sets theory

Abstract

Discovering the most effective anti-SARS-CoV-2 drugs is the optimal solution to get back to a normal life without COVID-19. Drug repurposing, also known as drug repositioning, has become one of the most important solutions for developing new COVID-19 drugs. However, this alternative requires long-term laboratory experiments to reach the optimal drug that involves the best combination of drug features to resist the COVID-19 virus. In response to this challenge, the COVID-19 drug repurposing (C19-DR) model based on pigeon-inspired optimizer (PIO) and rough sets theory (RST) is proposed. The proposed model presents a new rough set-based feature selection technique that uses a pigeon-inspired optimizer algorithm to find and validate the optimal reduct of drug features to design an effective COVID-19 drug. Moreover, the proposed model can investigate the efficiency of multiple medications against the COVID-19 virus based on the half-maximal inhibitory concentration (IC50) threshold. The effectiveness of the proposed COVID-19 drug repurposing model has been validated using a laboratory drug dataset consisting of 60 medications. The practical results show that the optimized rough set reduct of {hydrogen bonding acceptor (HBA) and number of chiral centers} is the most significant reduct that can be used to design an effective COVID-19 drug. Moreover, the proposed drug design model could verify the efficiency of a selected dataset of drug models based on evaluating the IC50 metric. The verification results proved the high effectiveness of the proposed model in evaluating the predicted IC50 with an accuracy of 91.4% and MSE of 0.034. These findings might be a promising solution that can assist researchers in developing and repurposing novel medications to treat COVID-19 and its new viral mutants.