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Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and

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Generative artificial intelligence (AI) accelerates drug discovery by creating novel molecules for faster, cheaper development. This review explores AI models for de novo drug design, highlighting recent advances and challenges.

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Area of Science:

  • Computational Chemistry and Cheminformatics
  • Artificial Intelligence in Drug Discovery
  • Medicinal Chemistry

Background:

  • Traditional drug discovery is costly and time-consuming, with high failure rates.
  • Artificial intelligence (AI) offers transformative solutions across pharmaceutical sciences.
  • Generative AI excels at creating novel data, including chemical molecules.

Purpose of the Study:

  • To review generative AI models for de novo drug design.
  • To discuss the fundamentals, frameworks, and applications of these AI models.
  • To explore recent advancements, challenges, and future potential of generative AI in drug discovery.

Main Methods:

  • Review of foundational and advanced generative AI models.
  • Analysis of AI applications in molecular property prediction, virtual screening, and molecule generation.
  • Examination of case studies and clinical assets developed using generative AI.

Main Results:

  • Generative AI models show significant promise in designing novel drug candidates.
  • AI accelerates the identification of molecules with desired pharmacokinetic and pharmacodynamic profiles.
  • Commercial partnerships are increasingly leveraging AI for drug development.

Conclusions:

  • Generative AI is revolutionizing de novo drug design, offering faster and more cost-effective development.
  • Continued research and development are crucial to fully realize AI's potential in creating new medicines.
  • AI-driven drug discovery is moving towards clinical applications, demonstrating its practical impact.