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<p>The drug discovery and development process is a complex, resource-intensive, and high-risk endeavour, traditionally characterized by long timelines, high costs, and low success rates. Recent advancements in artificial intelligence (AI) have transformed this landscape by enabling data-driven, predictive, and efficient strategies across the entire drug development pipeline. AI technologies, including machine learning (ML), deep learning (DL), and neural networks, have been employed for target identification, lead discovery, structure-based drug design, ADME/T prediction, toxicity assessment, and de novo molecule generation. AI-driven tools and platforms, such as DeepChem, AlphaFold, and IBM Watson, facilitate virtual screening, chemical space exploration, and patient-specific clinical trial optimization. Additionally, AI has revolutionized advanced applications in drug delivery, including nanorobotics and synergistic combination therapies, and has enhanced nanomedicine formulations for improved efficacy and safety. Despite its transformative potential, AI adoption faces challenges, including data scarcity, high costs, skill gaps, regulatory constraints, and the black box phenomenon. Nevertheless, with appropriate strategies, AI promises to accelerate drug discovery, reduce costs, improve success rates, and enable the development of safer and more effective therapeutics, marking a paradigm shift in pharmaceutical research and development.</p>

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Artificial intelligence and machine learning in drug discovery and development: a comprehensive review

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月26日

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  • Krushna A. Chaudhari,
  • Kedar S. Mathpati,
  • Ashish R. Alte

摘要

The drug discovery and development process is a complex, resource-intensive, and high-risk endeavour, traditionally characterized by long timelines, high costs, and low success rates. Recent advancements in artificial intelligence (AI) have transformed this landscape by enabling data-driven, predictive, and efficient strategies across the entire drug development pipeline. AI technologies, including machine learning (ML), deep learning (DL), and neural networks, have been employed for target identification, lead discovery, structure-based drug design, ADME/T prediction, toxicity assessment, and de novo molecule generation. AI-driven tools and platforms, such as DeepChem, AlphaFold, and IBM Watson, facilitate virtual screening, chemical space exploration, and patient-specific clinical trial optimization. Additionally, AI has revolutionized advanced applications in drug delivery, including nanorobotics and synergistic combination therapies, and has enhanced nanomedicine formulations for improved efficacy and safety. Despite its transformative potential, AI adoption faces challenges, including data scarcity, high costs, skill gaps, regulatory constraints, and the black box phenomenon. Nevertheless, with appropriate strategies, AI promises to accelerate drug discovery, reduce costs, improve success rates, and enable the development of safer and more effective therapeutics, marking a paradigm shift in pharmaceutical research and development.