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Artificial Intelligence (AI) accelerates drug discovery by improving efficiency and accuracy. This review explores AI

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Area of Science:

  • Pharmacology and Computational Chemistry
  • Biotechnology and Bioinformatics

Background:

  • Traditional drug development is lengthy, costly, and has a low success rate.
  • Massive data generation in drug discovery requires advanced analytical methods.
  • Artificial Intelligence (AI) offers improved efficiency and accuracy in drug discovery.

Purpose of the Study:

  • To review the drug discovery and development timeline.
  • To explore various drug design approaches.
  • To highlight the application of AI in drug discovery.

Main Methods:

  • Examination of traditional drug development and its limitations.
  • Introduction to AI-based technologies, including Machine Learning (ML) and Deep Learning (DL).
  • Presentation of big data research examples and available AI/ML tools and databases.

Main Results:

  • AI enhances decision-making through high-quality data utilization.
  • AI reduces the time and cost associated with drug development.
  • Review covers advanced ML/DL methods, databases, toolkits, and model evaluation.

Conclusions:

  • AI-driven methods are increasingly favored for drug discovery.
  • This review serves as a guide for researchers in AI-based drug development.
  • AI integration promises to revolutionize pharmaceutical research and development.