Machine learning enabled identification of potential SARS-CoV-2 3CLpro inhibitors based on fixed molecular fingerprints and Graph-CNN neural representations.
Machine learning enabled identification of potential SARS-CoV-2 3CLpro inhibitors based on fixed molecular fingerprints and Graph-CNN neural representations.
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DOI：
10.1016/j.jbi.2021.103821
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发表时间：
2021-07
影响因子：
4.5
通讯作者：
Delijewski M
中科院分区：
文献类型：
--
作者：
Haneczok J;Delijewski M
快速发展的人工智能和机器学习（ML）技术可以加快治疗开发，在当前的大流行时期，它们的优点尤其受到关注。本研究的目的是探索各种ML方法的分子性质预测，并说明其效用，以确定潜在的SARS-CoV-2 3CLpro抑制剂。我们基于以不同方式对分子表示进行操作的监督ML模型进行了一系列药物发现筛选，包括基于固定分子指纹的浅层学习方法，具有自学习分子表示的图卷积神经网络（Graph-CNN），以及基于组合固定和Graph-CNN学习表示的ML方法。我们的ML模型的结果在基于支架分割的ROC-AUC方面的聚合预测性能以及个体预测的粒度水平上进行了比较，对应于排名最高的再利用候选者。这一比较揭示了化学和药理学分类的某些特征同质性，磺胺类和抗癌药物的流行，以及确定了新的抗COVID-19潜在候选药物组。一系列用于分子性质预测的ML方法使药物发现筛选成为可能，说明了COVID-19的实用性。我们表明，所获得的结果与已经发表的COVID-19治疗研究相一致，并为从体外数据推断的潜在抗病毒特征提供了新的见解。
Rapidly developing AI and machine learning (ML) technologies can expedite therapeutic development and in the time of current pandemic their merits are particularly in focus. The purpose of this study was to explore various ML approaches for molecular property prediction and illustrate their utility for identifying potential SARS-CoV-2 3CLpro inhibitors. We perform a series of drug discovery screenings based on supervised ML models operating in different ways on molecular representations, encompassing shallow learning methods based on fixed molecular fingerprints, Graph Convolutional Neural Network (Graph-CNN) with its self-learned molecular representations, as well as ML methods based on combining fixed and Graph-CNN learned representations. Results of our ML models are compared both with respect to the aggregated predictive performance in terms of ROC-AUC based on the scaffold splits, as well as on the granular level of individual predictions, corresponding to the top ranked repurposing candidates. This comparison reveals both certain characteristic homogeneity regarding chemical and pharmacological classification, with a prevalence of sulfonamides and anticancer drugs, as well as identifies novel groups of potential drug candidates against COVID-19. A series of ML approaches for molecular property prediction enables drug discovery screenings, illustrating the utility for COVID-19. We show that the obtained results correspond well with the already published research on COVID-19 treatment, as well as provide novel insights on potential antiviral characteristics inferred from in vitro data.
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DOI：
10.1021/ci010132r
发表时间：
2002-11-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Durant, JL;Leland, BA;Nourse, JG
通讯作者：
Nourse, JG
影响因子：
6.1
作者：
Kandeel, Mahmoud;Al-Nazawi, Mohammed
通讯作者：
Al-Nazawi, Mohammed
影响因子：
8.4
作者：
Amzat, Jimoh;Aminu, Kafayat;Danjibo, Maryann C.
通讯作者：
Danjibo, Maryann C.
影响因子：
3.2
作者：
Hogan, Reed B., II;Hogan, Reed B., III;Dooley, Thomas P.
通讯作者：
Dooley, Thomas P.
影响因子：
5.6
作者：
Fischer, Andre;Sellner, Manuel;Lill, Markus A.
通讯作者：
Lill, Markus A.