Machine Learning Models Identify Inhibitors of SARS-CoV-2,Journal of Chemical Information and Modeling

当前位置： X-MOL 学术 › J. Chem. Inf. Model. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Machine Learning Models Identify Inhibitors of SARS-CoV-2
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2021-08-13 , DOI: 10.1021/acs.jcim.1c00683
Victor O Gawriljuk ₁ , Phyo Phyo Kyaw Zin ₂ , Ana C Puhl ₂ , Kimberley M Zorn ₂ , Daniel H Foil ₂ , Thomas R Lane ₂ , Brett Hurst _{3,

4} , Tatyana Almeida Tavella ₅ , Fabio Trindade Maranhão Costa ₅ , Premkumar Lakshmanane ₆ , Jean Bernatchez ₇ , Andre S Godoy ₁ , Glaucius Oliva ₁ , Jair L Siqueira-Neto ₇ , Peter B Madrid ₈ , Sean Ekins ₂

Affiliation

With the rapidly evolving SARS-CoV-2 variants of concern, there is an urgent need for the discovery of further treatments for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need, and numerous compounds have already been selected for in vitro testing by several groups. These have led to a growing database of molecules with in vitro activity against the virus. Machine learning models can assist drug discovery through prediction of the best compounds based on previously published data. Herein, we have implemented several machine learning methods to develop predictive models from recent SARS-CoV-2 in vitro inhibition data and used them to prioritize additional FDA-approved compounds for in vitro testing selected from our in-house compound library. From the compounds predicted with a Bayesian machine learning model, lumefantrine, an antimalarial was selected for testing and showed limited antiviral activity in cell-based assays while demonstrating binding (K_d 259 nM) to the spike protein using microscale thermophoresis. Several other compounds which we prioritized have since been tested by others and were also found to be active in vitro. This combined machine learning and in vitro testing approach can be expanded to virtually screen available molecules with predicted activity against SARS-CoV-2 reference WIV04 strain and circulating variants of concern. In the process of this work, we have created multiple iterations of machine learning models that can be used as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 with over 500 compounds is now freely available at www.assaycentral.org.

中文翻译：

机器学习模型识别 SARS-CoV-2 的抑制剂

随着人们关注的 SARS-CoV-2 变体迅速发展，迫切需要发现冠状病毒病 (COVID-19) 的进一步治疗方法。药物再利用是解决这一需求的最快速的策略之一，并且已经选择了许多化合物进行体外测试。这些导致具有体外抗病毒活性的分子数据库不断增加。机器学习模型可以通过基于先前发布的数据预测最佳化合物来帮助药物发现。在这里，我们实施了几种机器学习方法，从最近的 SARS-CoV-2体外抑制数据中开发预测模型，并使用它们来优先考虑其他 FDA 批准的化合物从我们的内部化合物库中选择的体外测试。从使用贝叶斯机器学习模型预测的化合物中，选择抗疟药苯芴醇进行测试，并在基于细胞的测定中显示出有限的抗病毒活性，同时使用微尺度热泳法证明与刺突蛋白的结合 ( K _d 259 nM)。我们优先考虑的其他几种化合物已经过其他人的测试，并且在体外也被发现具有活性。这结合了机器学习和体外测试方法可以扩展到虚拟筛选具有预测活性的可用分子，以对抗 SARS-CoV-2 参考 WIV04 菌株和关注的循环变体。在这项工作的过程中，我们创建了机器学习模型的多次迭代，可用作 SARS-CoV-2 抗病毒药物发现计划的优先级工具。包含 500 多种化合物的最新 SARS-CoV-2 模型现已在 www.assaycentral.org 免费提供。

更新日期：2021-09-27

点击分享查看原文

点击收藏

公开下载

阅读更多本刊最新论文本刊介绍/投稿指南11