Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking

Abstract

Background: The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has spread rapidly around the globe during the past 3 months. As the virus infected cases and mortality rate of this disease is increasing exponentially, scientists and researchers all over the world are relentlessly working to understand this new virus along with possible treatment regimens by discovering active therapeutic agents and vaccines. So, there is an urgent requirement of new and effective medications that can treat the disease caused by SARS-CoV-2.

Methods and findings: We perform the study of drugs that are already available in the market and being used for other diseases to accelerate clinical recovery, in other words repurposing of existing drugs. The vast complexity in drug design and protocols regarding clinical trials often prohibit developing various new drug combinations for this epidemic disease in a limited time. Recently, remarkable improvements in computational power coupled with advancements in Machine Learning (ML) technology have been utilized to revolutionize the drug development process. Consequently, a detailed study using ML for the repurposing of therapeutic agents is urgently required. Here, we report the ML model based on the Naive Bayes algorithm, which has an accuracy of around 73% to predict the drugs that could be used for the treatment of COVID-19. Our study predicts around ten FDA approved commercial drugs that can be used for repurposing. Among all, we found that 3 of the drugs fulfils the criterions well among which the antiretroviral drug Amprenavir (DrugBank ID-DB00701) would probably be the most effective drug based on the selected criterions.

Conclusions: Our study can help clinical scientists in being more selective in identifying and testing the therapeutic agents for COVID-19 treatment. The ML based approach for drug discovery as reported here can be a futuristic smart drug designing strategy for community applications.

Fig 1. Representation of the variation in the molecular weights of the considered active and inactive pharmacophore fingerprints.

Fig 2. Flowchart of the machine learning based approach to predict FDA approved drugs for COVID-19.

Fig 3

(a) The comparison of the false positive rate and true positive rate for all algorithms used in case of preparation of machine learning models (b) Chemical structure of the Atazanavir.

Fig 4

(a) Docking pose of SARS-CoV 3C- like Protease complexed with Amprenavir, (b) ligplot analysis showing possible bonds between protein and ligand.