Predicting commercially available antiviral drugs that may act on the novel coronavirus (2019-nCoV), Wuhan, China through a drug-target interaction deep learning model

Bo Ram Beck; Bonggun Shin; Yoonjung Choi; Sungsoo Park; Keunsoo Kang

The 2019-nCoV 3C-like proteinase was predicted to bind with atazanavir (Kd 94.94 nM), followed by efavirenz, ritonavir, and other antiviral drugs that have a predicted affinity of Kd > 100 nM potency (Table 1 ). No other protease inhibitor antiviral drug was found in the Kd < 1,000 nM range. Although there is . CC-BY-NC-ND 4.0 International license author/funder. It is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org /10.1101 /10. /2020 no real-world evidence about whether these drugs will act as predicted against 2019-nCoV yet, some case studies have been identified. For example, a docking study of lopinavir along with other HIV proteinase inhibitors of the CoV proteinase (PDBID 1UK3) suggests atazanavir and ritonavir, which are listed in the present prediction results, may inhibit the CoV proteinase in line with the inhibitory potency of lopinavir (14) . According to the prediction, viral proteinase-targeting drugs were predicted to act more favorably on the viral replication process than viral proteinase through the DTI model (Table 2 -6) . The results include antiviral drugs other than proteinase inhibitors, such as guanosine analogues (e.g., acyclovir, ganciclovir, and penciclovir), reverse transcriptase inhibitors, and integrase inhibitors.