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大流行药物的流行速度：在高性能计算机上通过基于机器学习和物理的混合模拟来加速COVID-19药物的发现
arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2021-03-04 , DOI: arxiv-2103.02843 Agastya P. Bhati, Shunzhou Wan, Dario Alfè, Austin R. Clyde, Mathis Bode, Li Tan, Mikhail Titov, Andre Merzky, Matteo Turilli, Shantenu Jha, Roger R. Highfield, Walter Rocchia, Nicola Scafuri, Sauro Succi, Dieter Kranzlmüller, Gerald Mathias, David Wifling, Yann Donon, Alberto Di Meglio, Sofia Vallecorsa, Heng Ma, Anda Trifan, Arvind Ramanathan, Tom Brettin, Alexander Partin, Fangfang Xia, Xiaotan Duan, Rick Stevens, Peter V. Coveney
arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2021-03-04 , DOI: arxiv-2103.02843 Agastya P. Bhati, Shunzhou Wan, Dario Alfè, Austin R. Clyde, Mathis Bode, Li Tan, Mikhail Titov, Andre Merzky, Matteo Turilli, Shantenu Jha, Roger R. Highfield, Walter Rocchia, Nicola Scafuri, Sauro Succi, Dieter Kranzlmüller, Gerald Mathias, David Wifling, Yann Donon, Alberto Di Meglio, Sofia Vallecorsa, Heng Ma, Anda Trifan, Arvind Ramanathan, Tom Brettin, Alexander Partin, Fangfang Xia, Xiaotan Duan, Rick Stevens, Peter V. Coveney
应对全球大流行挑战的竞赛提醒我们，现有的药物发现过程昂贵，低效且缓慢。筛选大量潜在的小分子以筛选用于抗病毒药物开发的先导化合物存在很大的瓶颈。加速药物发现的新机遇在于机器学习方法（基于线性加速器）与基于物理学的方法之间的接口。两种计算机方法各有各的优点和局限性，有趣的是，它们相互补充。在这里，我们提出了一种创新的方法，该方法结合了两种方法来加速药物发现。最终工作流程的规模取决于高性能计算。
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更新日期：2021-03-05
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