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In STAR protocols

Drug repositioning represents a cost- and time-efficient strategy for drug development. Artificial intelligence-based algorithms have been applied in drug repositioning by predicting drug-target interactions in an efficient and high throughput manner. Here, we present a workflow of in silico drug repositioning for host-based antivirals using specially defined targets, a refined list of drug candidates, and an easily implemented computational framework. The workflow described here can also apply to more general purposes, especially when given a user-defined druggable target gene set. For complete details on the use and execution of this protocol, please refer to Li et al. (2021).

Li Zexu, Yao Yingjia, Cheng Xiaolong, Li Wei, Fei Teng


Bioinformatics, High Throughput Screening, Immunology, Microbiology, Molecular Biology, Structural Biology