A computational framework of host-based drug repositioning for broad-spectrum antivirals against RNA viruses

RNA viruses are responsible for many zoonotic diseases that post great challenges for public health. Effective therapeutics against these viral infections remain limited. Here, we deployed a computational framework for host-based drug repositioning to predict potential antiviral drugs from 2,352 approved drugs and 1,062 natural compounds embedded in herbs of traditional Chinese medicine. By systematically interrogating public genetic screening data, we comprehensively cataloged host dependency genes (HDGs) that are indispensable for successful viral infection corresponding to 10 families and 29 species of RNA viruses. We then utilized these HDGs as potential drug targets and interrogated extensive drug-target interactions through database retrieval, literature mining, and de novo prediction using artificial intelligence-based algorithms. Repurposed drugs or natural compounds were proposed against many viral pathogens such as coronaviruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), flaviviruses, and influenza viruses. This study helps to prioritize promising drug candidates for in-depth evaluation against these virus-related diseases. [Display omitted] •Host dependency genes for RNA viruses are systematically cataloged•Repositioned drug candidates target host dependency genes for antiviral purpose•Artificial intelligence-based algorithms help to predict drug-target interactions•Prioritized antiviral drug candidates are proposed against RNA viruses Molecular Biology; Bioinformatics; Pharmacoinformatics