Designing Potential Inhibitors For SARS-CoV-2 Main Protease Using Deep Learning

Abstract

in this work we are trying to speed up the process of finding a cure for SARS-CoV-2 since SARS-CoV-2 have impacted our society due to the global pandemic which have affected the education, economy, world heath care and deaths caused by the virus due to the long time taken by drug discovery pipeline which is between 10 to 12 yeas for a drug to be develop and the enormous cost of the drug discovery pipeline and the low success rate of drug passing the FDA approve in the have motivated us to design a deep learning solution for designing a drug in fraction of the time required and for the current event done by the SARS-CoV-2 the target for this proposed solution will be SARS-CoV-2 the proposed solution is consist of two model one for generative molecules and the other for predicting the affinity of the molecule toward SARS-CoV-2 the proposed solution achieved a generation of molecules with average affinity of -9.8 and a prediction of accuracy of 98.1625% toward SARS-CoV-2, the proposed solution could reduce the drug discovery pipeline which is between 10 to 12 yeas to only 1 to 3 yeas for any novel virus.