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Artificial Intelligence in Drug Discovery and Development: Transforming Pharmaceutical Innovation.

Mohd Shoab Ali1, Taha Alqahtani2, Humood Al Shmrany3

  • 1Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India.

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Artificial intelligence (AI) accelerates drug discovery by improving decision-making in target identification, hit finding, and lead optimization. This technology enhances early predictability, reducing late-stage failures and advancing pharmaceutical development.

Keywords:
artificial intelligencedeep learningdrug discoveryin silico modelingmachine learningpharmaceutical development

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Area of Science:

  • Pharmaceutical Sciences
  • Computational Biology
  • Drug Discovery

Background:

  • Drug discovery is a lengthy, expensive, and high-risk process, often exceeding a decade from target identification to clinical application.
  • Traditional methods face significant challenges in efficiency and success rates.

Purpose of the Study:

  • To critically examine the evolving role of Artificial Intelligence (AI) in modern drug discovery.
  • To highlight AI's impact on pharmaceutical formulation and personalized medicine.
  • To discuss challenges and future directions for AI in pharmaceutics.

Main Methods:

  • Review of advancements in machine learning, deep learning, and natural language processing for drug discovery.
  • Examination of AI-driven insilico platforms for predicting toxicity, pharmacokinetics, and developability.
  • Analysis of collaborative models between AI developers and the pharmaceutical industry.

Main Results:

  • AI significantly enhances efficiency and accuracy in target identification, hit finding, lead optimization, and drug repurposing.
  • AI platforms improve early-stage predictability, reducing attrition rates in late-stage development.
  • AI is expanding its impact on pharmaceutical formulation and personalized medicine.

Conclusions:

  • AI is revolutionizing drug discovery, offering unprecedented speed and precision.
  • Addressing challenges like algorithmic transparency, data quality, and regulatory acceptance is crucial for realizing AI's full potential.
  • Collaboration and continued innovation are key to accelerating AI-driven translational outcomes in pharmaceutics.