Recent explosive growth of ‘make-on-demand’ chemical libraries brought unprecedented opportunities but also significant challenges to the field of computer-aided drug discovery. To address this expansion of the accessible chemical universe, molecular docking needs to accurately rank billions of chemical structures, calling for the development of automated hit-selecting protocols to minimize human intervention and error. Herein, we report the development of an artificial intelligence-driven virtual screening pipeline that utilizes Deep Docking with Autodock GPU, Glide SP, FRED, ICM and QuickVina2 programs to screen 40 billion molecules against SARS-CoV-2 main protease (Mpro). This campaign returned a significant number of experimentally confirmed inhibitors of Mpro enzyme, and also enabled to benchmark the performance of twenty-eight hit-selecting strategies of various degrees of stringency and automation. These findings provide new starting scaffolds for hit-to-lead optimization campaigns against Mpro and encourage the development of fully automated end-to-end drug discovery protocols integrating machine learning and human expertise.

中文翻译：

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通过共识自动发现 SARS-CoV-2 主要蛋白酶的非共价抑制剂 400 亿个小分子的深度对接

最近“按需生产”化学库的爆炸式增长给计算机辅助药物发现领域带来了前所未有的机遇，但也带来了重大挑战。为了解决可访问的化学宇宙的扩展问题，分子对接需要准确地对数十亿种化学结构进行排序，这就需要开发自动命中选择协议，以最大限度地减少人为干预和错误。在此，我们报告了人工智能驱动的虚拟筛选管道的开发，该管道利用 Autodock GPU、Glide SP、FRED、ICM 和 QuickVina2 程序的深度对接来筛选 400 亿个针对 SARS-CoV-2 主要蛋白酶 (Mpro) 的分子。该活动返回了大量经实验证实的 Mpro 酶抑制剂，并且还能够对 28 种不同严格度和自动化程度的命中选择策略的性能进行基准测试。这些发现为针对 Mpro 的从命中到先导的优化活动提供了新的起始支架，并鼓励开发集成机器学习和人类专业知识的全自动端到端药物发现协议。