Structure-based drug repurposing: Traditional and advanced AI/ML-aided methods.
Drug Discov Today
; 27(7): 1847-1861, 2022 07.
Article
in English
| MEDLINE | ID: covidwho-1739667
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Drug Repositioning
/
COVID-19 Drug Treatment
Limits:
Humans
Language:
English
Journal:
Drug Discov Today
Journal subject:
Pharmacology
/
Drug Therapy
Year:
2022
Document Type:
Article
Affiliation country:
J.drudis.2022.03.006
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