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

Choudhury C, Arul Murugan N, Deva Priyakumar U

Drug Discov Today - (-) - [2022-03-14; online 2022-03-14]

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.

Type: Review

PubMed 35301148

DOI 10.1016/j.drudis.2022.03.006

Crossref 10.1016/j.drudis.2022.03.006

pii: S1359-6446(22)00112-X
pmc: PMC8920090

Publications 8.1.0