PT  - JOURNAL ARTICLE
AU  - Kaifu Gao
AU  - Duc Duy Nguyen
AU  - Rui Wang
AU  - Guo-Wei Wei
TI  - Machine intelligence design of 2019-nCoV drugs
AID  - 10.1101/2020.01.30.927889
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.01.30.927889
4099  - http://biorxiv.org/content/early/2020/02/04/2020.01.30.927889.short
4100  - http://biorxiv.org/content/early/2020/02/04/2020.01.30.927889.full
AB  - Wuhan coronavirus, called 2019-nCoV, is a newly emerged virus that infected more than 9692 people and leads to more than 213 fatalities by January 30, 2020. Currently, there is no effective treatment for this epidemic. However, the viral protease of a coronavirus is well-known to be essential for its replication and thus is an effective drug target. Fortunately, the sequence identity of the 2019-nCoV protease and that of severe-acute respiratory syndrome virus (SARS-CoV) is as high as 96.1%. We show that the protease inhibitor binding sites of 2019-nCoV and SARS-CoV are almost identical, which means all potential anti-SARS-CoV chemotherapies are also potential 2019-nCoV drugs. Here, we report a family of potential 2019-nCoV drugs generated by a machine intelligence-based generative network complex (GNC). The potential effectiveness of treating 2019-nCoV by using some existing HIV drugs is also analyzed.