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Updated: Sep 12, 2025

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Generative artificial intelligence based models optimization towards molecule design enhancement.

Tarek Khater1, Sara Awni Alkhatib1,2, Aamna AlShehhi1

  • 1Department of Biomedical Engineering and Biotechnology, Khalifa University, P.O. Box 127788, Abu Dhabi, United Arab Emirates.

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|August 4, 2025
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Summary
This summary is machine-generated.

Generative artificial intelligence (GenAI) models accelerate drug discovery by designing novel molecules. This review details techniques to improve GenAI accuracy, validity, and drug-like properties for enhanced molecular design.

Keywords:
Chemical informaticsDrug discoveryGenerative AIMolecular designOptimizationReinforcement learning

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Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Artificial Intelligence

Background:

  • Generative artificial intelligence (GenAI) offers powerful capabilities for designing novel molecules in drug discovery.
  • Current GenAI applications face challenges in prediction accuracy, molecular validity, and optimizing for drug-like properties.

Purpose of the Study:

  • To provide a comprehensive analysis of techniques enhancing GenAI performance in molecular design.
  • To explore advancements and address limitations in GenAI-driven drug discovery.

Main Methods:

  • Review of key generative architectures: variational autoencoders, generative adversarial networks, and transformer-based models.
  • Discussion of advancements: reinforcement learning, multi-objective optimization, and integration of chemical knowledge.
  • Examination of challenges: data quality, model interpretability, and objective functions.

Main Results:

  • GenAI architectures contribute uniquely to diverse molecular design.
  • Advanced techniques improve molecular validity, novelty, and drug-likeness.
  • Persistent challenges require further research for optimal GenAI application.

Conclusions:

  • GenAI is a transformative tool for drug discovery, enabling the design of complex molecules.
  • Strategic guidance is provided to overcome limitations and enhance GenAI's role in molecular design.
  • This review serves as a key resource for researchers utilizing GenAI in pharmaceutical development.