Structure-based drug repurposing: Traditional and advanced AI/ML-aided methods
Authors
Chinmayee Choudhury
aDepartment of Experimental Medicine and Biotechnology, Postgraduate Institute of Medical Education and Research, Sector-12, Chandigarh 160012, India
N Murugan
bDepartment of Computer Science, School of Electrical Engineering and Computer Sciences, KTH Royal Institute of Technology, S-100 44, Stockholm, Sweden
U Priyakumar
dCenter for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad 500 032, India
Keywords:
Drug repurposing, Machine learning, Force field, Quantum mechanics, Inverse design, Generative modeling
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: Drug repurposing, Machine learning, Force field, Quantum mechanics, Inverse design, Generative modeling
Author Biography
N Murugan, bDepartment of Computer Science, School of Electrical Engineering and Computer Sciences, KTH Royal Institute of Technology, S-100 44, Stockholm, Sweden
cDepartment of Computational Biology, Indraprastha Institute of Information Technology, New Delhi 110020, India
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