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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Structure-based drug repurposing: Traditional and advanced AI/ML-aided methods.

Chinmayee Choudhury1, N Arul Murugan2, U Deva Priyakumar3

  • 1Department of Experimental Medicine and Biotechnology, Postgraduate Institute of Medical Education and Research, Sector-12, Chandigarh 160012, India.

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The COVID-19 pandemic necessitates faster drug discovery. This review explores computational and AI methods for efficient structure-based drug repurposing, including generative models for novel lead compounds.

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Drug repurposingForce fieldGenerative modelingInverse designMachine learningQuantum mechanics

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Artificial intelligence in medicine

Background:

  • The COVID-19 pandemic underscores the need for rapid drug discovery pipelines.
  • Traditional high-throughput screening (HTS) is resource-intensive and time-consuming.
  • Computational methods offer a cost-effective and efficient alternative for identifying drug leads.

Purpose of the Study:

  • To review traditional and AI-based computational methods for structure-based drug discovery (SBDD).
  • To highlight the application of AI in screening repurposable chemical spaces.
  • To discuss the role of generative models in de novo drug design.

Main Methods:

  • Structure-based drug repurposing (SBDR) as an in silico approach.
  • Application of artificial intelligence (AI) algorithms for rapid screening.
  • Utilizing generative models to create novel molecular scaffolds.

Main Results:

  • AI and computational methods accelerate the identification of lead compounds.
  • Structure-based drug repurposing is a viable strategy for efficient drug discovery.
  • Generative models can design molecules from existing repurposable chemical libraries.

Conclusions:

  • Computational and AI-driven approaches are crucial for modern drug discovery pipelines.
  • Structure-based drug repurposing significantly reduces time and resources.
  • AI, particularly generative models, offers innovative solutions for identifying and designing drug candidates.