In Silico Drug Discovery for COVID-19 Using an Unsupervised Feature Extraction Method

Abstract

In silico drug discovery is useful for screening and identifying large numbers of drug candidate compounds in a way that is not possible using classical experimental approaches. Dr Y-H Taguchi at Chuo University, Japan, has developed a computational technique known as ‘tensor decomposition-based unsupervised feature extraction’. He has successfully applied this as an in silico phenotype-based drug discovery method to repurpose known drugs for severe acute respiratory syndrome coronavirus 2 and has successfully identified various known anti-viral drugs as viable candidates for the successful treatment of COVID-19.