Structure-based drug repurposing: Traditional and advanced AI/ML-aided methods

Drug Discov Today. 2022 Jul;27(7):1847-1861. doi: 10.1016/j.drudis.2022.03.006. Epub 2022 Mar 14.


The current global health emergency in the form of the Coronavirus 2019 (COVID-19) pandemic has highlighted the need for fast, accurate, and efficient drug discovery pipelines. Traditional drug discovery projects relying on in vitro high-throughput screening (HTS) involve large investments and sophisticated experimental set-ups, affordable only to big biopharmaceutical companies. In this scenario, application of efficient state-of-the-art computational methods and modern artificial intelligence (AI)-based algorithms for rapid screening of repurposable chemical space [approved drugs and natural products (NPs) with proven pharmacokinetic profiles] to identify the initial leads is a powerful option to save resources and time. Structure-based drug repurposing is a popular in silico repurposing approach. In this review, we discuss traditional and modern AI-based computational methods and tools applied at various stages for structure-based drug discovery (SBDD) pipelines. Additionally, we highlight the role of generative models in generating molecules with scaffolds from repurposable chemical space.

Keywords: Drug repurposing; Force field; Generative modeling; Inverse design; Machine learning; Quantum mechanics.

Publication types

  • Review
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Artificial Intelligence
  • COVID-19 Drug Treatment*
  • Drug Discovery
  • Drug Repositioning*
  • Humans
  • Pandemics