Machine intelligence design of 2019-nCoV drugs

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

A cluster of pneumonia cases of unknown cause emerged with connections to Huanan South China Seafood Market in Wuhan city, Hubei Province of China in late December 2019. On January 7, 2020, a positive-sense, single-stranded RNA coronavirus (CoV) was identified as the causative agent, and the World Health Organization (WHO) named this novel coronavirus as 2019-nCoV on January 10. By January 30, a total of 9692 confirmed cases with 213 deaths had been reported in the world. As reported in, 1 in comparison with the basic reproduction number (R 0 ) of the severe acute respiratory syndrome (SARS) epidemic, which is about 4.91, R 0 of the 2019-nCoV epidemic was estimated as high as 6.47, which implies the severity of this widespread dissemination caused by 2019-nCoV. Under the current health emergency, many researchers around the world engaged in the investigation of the genetic and functional data of 2019-nCoV and compare to other coronaviruses to design proper infection control strategy 2, 3 and seek potential drugs that can prevent and/or cure this serious epidemic. [4] [5] [6] [7] On January 10, the genome sequence of 2019-nCoV was first released on GenBank (accession MN908947) by Yong-Zhen Zhang's group at Fudan University. 8 Subsequently, phylogenetic analysis revealed that 2019-nCoV belonged to the Betacoronavirus genera and its closest whole-genome relatives are two SARS-like coronaviruses from bats, i.e., ZC45 and ZXC21, which shared about 89% sequence identity with 2019-nCoV. 6, 7 Moreover, detailed sequence alignment data indicates that there is significant genetics distance between the 2019-nCoV and the human SARS-CoV. Therefore, the 2019-nCoV should be considered as a new type of bat coronavirus. However, this observation raised a worth-thinking issue whether the 2019-nCoV has the same infective mechanisms as the human SARS-CoV.