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一种基于深度学习的药物,通过向细胞进入机制,重新利用抗SARS-CoV-2化合物的查和验证

Yingjia Yao¹, Yunhan Zhang¹, Zexu Li¹

¹Key Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, Northeastern University, Shenyang, 110819, China; National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang, 110819, China; Key Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Ministry of Education, Shenyang, 110819, China.

Biochemical and biophysical research communications

|July 19, 2023

概括

这项研究使用深度学习来选现有药物和传统中医药用于针对病毒进入的COVID-19治疗方法. 一些化合物,包括Fostamatinib,有效地阻止了SARS-CoV-2细胞进入.

科学领域:

病毒学病毒学
药理学药理学是指药理学的学科.
计算生物学计算生物学

背景情况:

由SARS-CoV-2引起的COVID-19大流行,对全球健康和经济构成重大威胁.
针对COVID-19的有效抗病毒治疗仍然有限,需要新的治疗策略.

研究的目的:

通过准病毒的细胞进入机制来识别潜在的抗SARS-CoV-2候选药物.
使用深度学习方法选FDA批准的药物和传统中医药的活性成分.

主要方法:

基于深度学习的药物重新定位策略被用于选2635种FDA批准的药物和1062种传统中医药成分.
进行了in silico分子对接,以验证候选化合物与宿主受体或病毒尖端蛋白之间的相互作用.
一个SARS-CoV-2伪病毒系统被用于实验性评估已识别的候选药物的有效性,阻止病毒细胞进入.

主要成果:

深度学习查确定了针对SARS-CoV-2细胞进入的潜在候选药物.
分子对接证实了顶级化合物与病毒或宿主目标之间的相互作用.
福斯塔马提尼布,林加利普丁,莱瑟戈尔和索福里丁在纳米分子度下显著抑制了SARS-CoV-2变种进入人类肺细胞的情况.

结论:

基于深度学习的药物重新定位是发现针对特定机制的抗病毒药物的可行策略.
包括Fostamatinib在内的已识别的化合物代表了COVID-19治疗开发的有希望的候选人.
这项研究为开发针对SARS-CoV-2及其变体的新疗法提供了宝贵的资源.

关键词:

在 COVID-19 疫情中,药物药物药物药物药物这就是SARS-CoV-2病毒.目标目标目标目标

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