Predicting Potential SARS-COV-2 Drugs-In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking.
Predicting Potential SARS-COV-2 Drugs-In Depth Drug Database Screening Using Deep Neural Network Framework SSnet, Classical Virtual Screening and Docking.
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预测潜在的SARS-COV-2药物——基于深度神经网络框架SSnet、经典虚拟筛选与对接的药物数据库筛选
DOI：
10.3390/ijms22041573
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发表时间：
2021-02-04
影响因子：
5.6
通讯作者：
Zoltowski B
中科院分区：
文献类型：
--
作者：
Karki N;Verma N;Trozzi F;Tao P;Kraka E;Zoltowski B
严重急性呼吸系统综合征冠状病毒2已经改变了全球范围内的生活。来自世界各地的研究实验室的共同努力导致了使用现有药物确定COVID-19的潜在药物治疗方法，以及多种疫苗的发现。在紧急危机期间，快速识别潜在的新疗法需要全球和跨学科合作，以及增强的开放获取研究模式，以传播新的想法和线索。在本文中，我们介绍了一种基于深度神经网络的药物筛选方法的应用，使用对接算法对已批准的药物进行药物再利用，并将筛选扩展到75万种化合物的大型库，以进行从头药物发现。大型库筛选的结果被纳入一个开放访问的网络界面，使来自不同领域的研究人员能够靶向感兴趣的分子。我们的组合方法允许识别可能能够重新利用的现有药物和重新设计ACE 2调节化合物。通过这些努力，我们展示了一种新的机器学习算法用于药物发现的实用性，SSnet，它可以作为一种工具来分类大型分子库，以识别具有可能疗效的分子类别。
Severe Acute Respiratory Syndrome Corona Virus 2 has altered life on a global scale. A concerted effort from research labs around the world resulted in the identification of potential pharmaceutical treatments for CoVID-19 using existing drugs, as well as the discovery of multiple vaccines. During an urgent crisis, rapidly identifying potential new treatments requires global and cross-discipline cooperation, together with an enhanced open-access research model to distribute new ideas and leads. Herein, we introduce an application of a deep neural network based drug screening method, validating it using a docking algorithm on approved drugs for drug repurposing efforts, and extending the screen to a large library of 750,000 compounds for de novo drug discovery effort. The results of large library screens are incorporated into an open-access web interface to allow researchers from diverse fields to target molecules of interest. Our combined approach allows for both the identification of existing drugs that may be able to be repurposed and de novo design of ACE2-regulatory compounds. Through these efforts we demonstrate the utility of a new machine learning algorithm for drug discovery, SSnet, that can function as a tool to triage large molecular libraries to identify classes of molecules with possible efficacy.
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