Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus.
Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus.
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DOI：
10.1021/acs.jcim.1c00460
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发表时间：
2021-08-23
影响因子：
5.6
通讯作者：
Ekins S
中科院分区：
文献类型：
--
作者：
Gawriljuk VO;Foil DH;Puhl AC;Zorn KM;Lane TR;Riabova O;Makarov V;Godoy AS;Oliva G;Ekins S
黄热病(YF)是一种由受感染蚊子传播的急性病毒性出血症。当病毒传入蚊子密度高、疫苗接种率低的人口稠密地区时，就会发生YF大流行。缺乏针对YF以及寨卡和登革热等同源感染的特定小分子药物治疗，突显了这些黄病毒作为公共卫生问题的重要性。随着计算机硬件和生物活性数据可用性的提高，基于机器学习方法的新工具被引入药物发现中，作为一种手段来利用产生的日益增长的高通量筛选(HTS)数据来降低成本和提高药物开发的速度。使用预测机器学习模型，使用先前公布的HTS运动数据或公共数据库中的数据，可以选择具有理想生物活性和吸收、分布、代谢和排泄特征的化合物。在这项研究中，我们从文献和公共数据库中整理了基于细胞的黄热病病毒检测数据。这些数据被用来建立带有几种机器学习方法的预测模型，这些方法可以对化合物进行体外测试。我们对五个分子进行了优先排序和体外测试，从中我们确定了一个新的吡唑磺酰胺衍生物，EC503.2μM和CC5024μM，这代表了一种适合于Hit-to-Lead优化的新支架，可以扩大YF可用的候选药物发现。
Yellow fever (YF) is an acute viral hemorrhagic disease transmitted by infected mosquitoes. Large epidemics of YF occur when the virus is introduced into heavily populated areas with high mosquito density and low vaccination coverage. The lack of a specific small molecule drug treatment against YF as well as for homologous infections, such as zika and dengue, highlights the importance of these flaviviruses as a public health concern. With the advancement in computer hardware and bioactivity data availability, new tools based on machine learning methods have been introduced into drug discovery, as a means to utilize the growing high throughput screening (HTS) data generated to reduce costs and increase the speed of drug development. The use of predictive machine learning models using previously published data from HTS campaigns or data available in public databases, can enable the selection of compounds with desirable bioactivity and absorption, distribution, metabolism and excretion profiles. In this study, we have collated cell-based assays data for yellow fever virus from literature and public databases. The data was used to build predictive models with several machine learning methods that could prioritize compounds for in vitro testing. Five molecules were prioritized and tested in vitro, from which we have identified a new pyrazolesulfonamide derivative with EC50 3.2 μM and CC50 24 μM, which represents a new scaffold suitable for hit-to-lead optimization that can expand the available drug discovery candidates for YF.
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