Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds
Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds
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DOI：
10.1002/minf.202000028
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发表时间：
2020-03-23
影响因子：
3.6
通讯作者：
Cherkasov, Artem
中科院分区：
文献类型：
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作者：
Anh-Tien Ton;Gentile, Francesco;Cherkasov, Artem
最近出现的 2019 新型冠状病毒 (SARS-CoV-2) 和相关的 COVID-19 疾病会导致严重甚至致命的呼吸道感染，但目前尚无批准的疗法或有效的治疗方法可以有效对抗疫情。这种紧急情况迫使世界各国开发针对 SARS-CoV-2 的新型疫苗或小分子疗法来应对。通过这些努力，SARS-CoV-2 主要蛋白酶 (Mpro) 的结构已被迅速解析并公开，以促进全球开发新候选药物的努力。最近，我们课题组开发了一种新型深度学习平台——深度对接（DD），它可以快速预测Glide（或任何其他对接程序）的对接分数，从而能够在短时间内对数十亿个可购买分子进行基于结构的虚拟筛选。在当前的研究中，我们将 DD 应用于 ZINC15 库中的所有 13 亿种化合物，以确定 SARS-CoV-2 Mpro 蛋白的前 1,000 个潜在配体。这些化合物已公开供科学界进一步表征和开发。
The recently emerged 2019 Novel Coronavirus (SARS-CoV-2) and associated COVID-19 disease cause serious or even fatal respiratory tract infection and yet no approved therapeutics or effective treatment is currently available to effectively combat the outbreak. This urgent situation is pressing the world to respond with the development of novel vaccine or a small molecule therapeutics for SARS-CoV-2. Along these efforts, the structure of SARS-CoV-2 main protease (Mpro) has been rapidly resolved and made publicly available to facilitate global efforts to develop novel drug candidates. Recently, our group has developed a novel deep learning platform - Deep Docking (DD) which provides fast prediction of docking scores of Glide (or any other docking program) and, hence, enables structure-based virtual screening of billions of purchasable molecules in a short time. In the current study we applied DD to all 1.3 billion compounds from ZINC15 library to identify top 1,000 potential ligands for SARS-CoV-2 Mpro protein. The compounds are made publicly available for further characterization and development by scientific community.