QSAR Modeling of SARS‐CoV Mpro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS‐CoV‐2,Molecular Informatics

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QSAR Modeling of SARS‐CoV Mpro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS‐CoV‐2
Molecular Informatics ( IF 2.8 ) Pub Date : 2020-07-28 , DOI: 10.1002/minf.202000113
Vinicius M Alves ₁ , Tesia Bobrowski ₂ , Cleber C Melo-Filho ₂ , Daniel Korn _{2,

3} , Scott Auerbach ₄ , Charles Schmitt ₁ , Eugene N Muratov _{2,

5} , Alexander Tropsha ₂

Affiliation

The main protease (M^pro) of the SARS‐CoV‐2 has been proposed as one of the major drug targets for COVID‐19. We have identified the experimental data on the inhibitory activity of compounds tested against the closely related (96 % sequence identity, 100 % active site conservation) M^pro of SARS‐CoV. We developed QSAR models of these inhibitors and employed these models for virtual screening of all drugs in the DrugBank database. Similarity searching and molecular docking were explored in parallel, but docking failed to correctly discriminate between experimentally active and inactive compounds, so it was not relied upon for prospective virtual screening. Forty‐two compounds were identified by our models as consensus computational hits. Subsequent to our computational studies, NCATS reported the results of experimental screening of their drug collection in SARS‐CoV‐2 cytopathic effect assay (https://opendata.ncats.nih.gov/covid19/). Coincidentally, NCATS tested 11 of our 42 hits, and three of them, cenicriviroc (AC₅₀ of 8.9 μM), proglumetacin (tested twice independently, with AC₅₀ of 8.9 μM and 12.5 μM), and sufugolix (AC₅₀ 12.6 μM), were shown to be active. These observations support the value of our modeling approaches and models for guiding the experimental investigations of putative anti‐COVID‐19 drug candidates. All data and models used in this study are publicly available via Supplementary Materials, GitHub (https://github.com/alvesvm/sars‐cov‐mpro), and Chembench web portal (https://chembench.mml.unc.edu/).

中文翻译：

SARS-CoV Mpro 抑制剂的 QSAR 模型确定 Sufugolix、Cenicriviroc、Proglumetacin 和其他药物可作为针对 SARS-CoV-2 再利用的候选药物

SARS-CoV-2的主要蛋白酶 (M ^pro ) 已被提议作为 COVID-19 的主要药物靶点之一。我们已经确定了测试化合物对密切相关（96% 序列同一性，100% 活性位点保守）M ^{pro的抑制活性的实验数据}SARS-CoV。我们开发了这些抑制剂的 QSAR 模型，并使用这些模型对 DrugBank 数据库中的所有药物进行虚拟筛选。相似性搜索和分子对接被并行探索，但对接未能正确区分实验活性和非活性化合物，因此不依赖于前瞻性虚拟筛选。我们的模型将 42 种化合物识别为共识计算命中。在我们的计算研究之后，NCATS 报告了在 SARS-CoV-2 细胞病变效应测定中对其药物收集的实验筛选结果 (https://opendata.ncats.nih.gov/covid19/)。巧合的是，NCATS 测试了我们 42 个命中中的 11 个，其中三个是 cenicriviroc（AC ₅₀为 8.9 μM）、proglumetacin（独立测试两次，使用 AC_{8.9 μM 和 12.5 μM 的50}个）和 sufugolix（AC ₅₀ 12.6 μM）显示有活性。这些观察结果支持了我们的建模方法和模型在指导推定的抗 COVID-19 候选药物的实验研究方面的价值。本研究中使用的所有数据和模型均可通过补充材料、GitHub ( https://github.com/alvesvm/sars‐cov‐mpro ) 和 Chembench 门户网站 ( https://chembench.mml.unc.edu ) 公开获得/）。

更新日期：2020-07-28

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