Author: Duc Duy Nguyen; Kaifu Gao; Jiahui Chen; Rui Wang; Guo-Wei Wei
Title: Potentially highly potent drugs for 2019-nCoV Document date: 2020_2_13
ID: g5wpa2ee_25
Snippet: ChEMBL [14] , an open database that brings chemical, bioactivity, and genomic data together to translate genomic information into effective new drugs, is employed to construct our 2019-nCoV training set. Considering the high sequence identity between viral proteases of 2019-nCoV and SARS-CoV, we take the protease of SARS-CoV as the input target in ChEMBL and a total 115 ChEMBL IDs of the target can be found. The experimental ∆G values of 2019-n.....
Document: ChEMBL [14] , an open database that brings chemical, bioactivity, and genomic data together to translate genomic information into effective new drugs, is employed to construct our 2019-nCoV training set. Considering the high sequence identity between viral proteases of 2019-nCoV and SARS-CoV, we take the protease of SARS-CoV as the input target in ChEMBL and a total 115 ChEMBL IDs of the target can be found. The experimental ∆G values of 2019-nCoV 115 SARS-CoV protease inhibition compounds range from −10.0 kcal/mol to 7.5 kcal/mol. We exclude compounds with positive values, resulting in a total of 84 SARS-nCoV protease inhibition compounds for our machine learning training. A collection of these 84 compounds is given in the Supplementary Materials.
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