Rapid Deployment of Antiviral Drugs Using Single-Virus Tracking and Machine Learning

被引：1

作者：

Zhu, Meng-Die ^{[1
]}

Shi, Xue-Hui ^{[1
]}

Wen, Hui-Ping ^{[1
]}

Chen, Li-Ming ^{[1
]}

Fu, Dan-Dan ^{[2
]}

Du, Lei ^{[2
]}

Li, Jing ^{[2
]}

Wan, Qian-Qian ^{[1
]}

Wang, Zhi-Gang ^{[1
]}

Yu, Chuanming ^{[1
]}

Pang, Dai-Wen ^{[1
]}

Liu, Shu-Lin ^{[1
]}

机构：

[1] State Key Laboratory of Medicinal Chemical Biology, Frontiers Science Center for New Organic Matter, Tianjin Key Laboratory of Biosensing and Molecular Recognition, Research Center for Analytical Sciences, College of Chemistry, School of Medicine, Frontier

[2] College of Chemistry and Molecular Sciences, Wuhan University, Wuhan

来源：

ACS Nano | 2024年 / 18卷 / 52期

基金：

中国国家自然科学基金;

关键词：

antiviral drugs; drug repurposing; machine learning; quantum dots; single-virus tracking;

D O I：

10.1021/acsnano.4c10136

中图分类号：

学科分类号：

摘要：

The outbreak of emerging acute viral diseases urgently requires the acceleration of specialized antiviral drug development, thus widely adopting phenotypic screening as a strategy for drug repurposing in antiviral research. However, traditional phenotypic screening methods typically require several days of experimental cycles and lack visual confirmation of a drug’s ability to inhibit viral infection. Here, we report a robust method that utilizes quantum-dot-based single-virus tracking and machine learning to generate unique single-virus infection fingerprint data from viral trajectories and detect the dynamic changes in viral movement following drug administration. Our findings demonstrated that this approach can successfully identify viral infection patterns at various infection phases and predict antiviral drug efficacy through machine learning within 90 min. This method provides valuable support for assessing the efficacy of antiviral drugs and offers promising applications for responding to future outbreaks of emerging viruses. © 2024 American Chemical Society.

引用

页码：35256 / 35268

页数：12

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