Potentially highly potent drugs for 2019-nCoV

Duc Duy Nguyen; Kaifu Gao; Jiahui Chen; Rui Wang; Guo-Wei Wei

Drug repositioning (also known as drug repurposing), which concerns the investigation of existing drugs for new therapeutic target indications, has emerged as a successful strategy for drug discovery due to the reduced costs and expedited approval procedures [3] [4] [5] . Several successful examples unveil its great values in practice: Nelfinavir, initially developed to treat the human immunodeficiency virus (HIV), is now being used for cancer treatments. Amantadine was firstly designed to treat influenza caused by type A influenza viral infection and is being used for Parkinson's disease later on [6] . In recent years, the rapid growth of drug-related datasets, as well as open data initiatives, has led to new developments for computational drug repositioning, particularly, structural-based drug repositioning (SBDR). Machine learning, network analysis, and text mining and semantic inference are three major computational approaches commonly applied in drug repositioning [7] . The rapid accumulation of genetic and structural databases [8] , the development of low-dimensional mathematical representations of complex biomolecular structures [9, 10] , and the availability of advanced deep learning algorithms have made machine learning-based drug reposition a promising approach [7] . Considering the urgent need for anti-2019-nCoV drugs, a computational drug repositioning is one of the most feasible strategies for discovering 2019-nCoV drugs.