Structure-based drug repurposing: Traditional and advanced AI/ML-aided methods. Issue 7 (July 2022)

• Record Type: Journal Article
• Authors: Choudhury, Chinmayee; Arul Murugan, N.; Priyakumar, U. Deva
• Is Part Of: Drug discovery today. Volume 27:Issue 7(2022)
• Page Start: 1847
• Page End: 1861
• Publication Date: 2022-07
• Subjects: Drug repurposing -- Machine learning -- Force field -- Quantum mechanics -- Inverse design -- Generative modeling; Drugs -- Design -- Periodicals; Drugs -- Research -- Periodicals; 615.1
• DOI: 10.1016/j.drudis.2022.03.006
• Languages: English
• ISSNs: 1359-6446

Abstract

Highlights: Repurposing existing drugs for new diseases is cost effective and time saving. In silico methods are crucial for rapid drug screening in the early stages. Machine learning algorithms confer speed and accuracy to computational screening approaches. Deep learning is immensely powerful to design molecules with desired properties. Abstract : 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.