An in silico drug repositioning workflow for host-based antivirals

STAR Protoc. 2021 Jul 7;2(3):100653. doi: 10.1016/j.xpro.2021.100653. eCollection 2021 Sep 17.

Abstract

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).

Keywords: Bioinformatics; High Throughput Screening; Immunology; Microbiology; Molecular Biology; Structural Biology.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Antiviral Agents / pharmacology*
  • Artificial Intelligence
  • Computational Biology / methods*
  • Computer Simulation*
  • Databases, Genetic
  • Databases, Pharmaceutical
  • Drug Repositioning / methods*
  • Host-Pathogen Interactions / drug effects
  • Host-Pathogen Interactions / genetics
  • Humans
  • Virus Diseases / virology
  • Viruses / drug effects
  • Workflow

Substances

  • Antiviral Agents