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On the application of artificial intelligence in virtual screening.

Thanawat Thaingtamtanha1, Rahul Ravichandran1, Francesco Gentile1,2

  • 1Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario, Canada.

Expert Opinion on Drug Discovery
|May 19, 2025
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Summary

Artificial intelligence (AI) is revolutionizing drug discovery by enhancing virtual screening (VS). AI improves ligand-based (LBVS) and structure-based (SBVS) methods, accelerating the identification of potential drug candidates.

Keywords:
Artificial intelligencechemical librariesdrug discoverymachine learningvirtual screening

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Area of Science:

  • Drug Discovery
  • Computational Chemistry
  • Bioinformatics

Background:

  • Artificial intelligence (AI) is a key technology in modern drug discovery.
  • Virtual screening (VS) is a critical early-stage process for identifying potential drug candidates.
  • AI significantly enhances both ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS).

Purpose of the Study:

  • To provide an overview of AI applications in drug discovery, focusing on LBVS and SBVS.
  • To highlight prospective cases where AI led to the identification and validation of new bioactive molecules.
  • To discuss the integration of AI with quantitative structure-activity relationship (QSAR) modeling and advanced SBVS techniques.

Main Methods:

  • Literature search of studies up to March 2025.
  • Review of AI applications in LBVS, including quantitative structure-activity relationship (QSAR) modeling.
  • Analysis of AI's role in enhancing structure-based virtual screening (SBVS) techniques like molecular docking and dynamics simulations.

Main Results:

  • AI is transforming VS by utilizing large datasets and improving scalability.
  • AI-driven LBVS and SBVS approaches have successfully identified and validated novel bioactive molecules.
  • AI enhances the efficiency and precision of drug candidate identification.

Conclusions:

  • AI shows immense potential to streamline and improve the drug discovery process.
  • Continued advancements in AI are crucial for overcoming challenges in data curation and model validation.
  • Effective integration of AI with experimental methods is key to realizing its full impact on drug discovery.