The Role of AI and Machine Learning in Accelerating Drug Discovery and Personalized Medicine

Liang Xu; Xiaoyu Zhang

doi:10.71222/v2qvxe86

Authors

Liang Xu School of Food and Biological Engineering, Zhengzhou University of Light Industry, Zhengzhou, China Author
Xiaoyu Zhang College of Biological & Environmental Sciences, Zhejiang Wanli University, Ningbo, China Author

DOI:

https://doi.org/10.71222/v2qvxe86

Keywords:

artificial intelligence, machine learning, drug discovery, personalized medicine, healthcare, drug repurposing, predictive diagnostics

Abstract

Artificial intelligence (AI) and machine learning (ML) are revolutionizing drug discovery and personalized medicine by offering powerful tools for analyzing complex biological data, predicting drug efficacy, and tailoring treatments to individual patients. This review paper explores the multifaceted roles of AI and ML in these domains, encompassing target identification, drug design, clinical trial optimization, and patient stratification. We begin with a historical overview of AI applications in healthcare, followed by a detailed examination of core themes such as AI-driven drug repurposing and the use of ML in predictive diagnostics. The paper further delves into the challenges and limitations of implementing AI/ML technologies, including data bias, interpretability issues, and regulatory hurdles. Finally, we discuss future perspectives, emphasizing the potential of AI to transform healthcare through enhanced precision, accelerated drug development timelines, and improved patient outcomes. This review synthesizes current research, highlights key advancements, and identifies critical areas for future investigation, aiming to provide a comprehensive understanding of the transformative impact of AI and ML on drug discovery and personalized medicine. The successful integration of these technologies promises to unlock new possibilities for preventing and treating diseases, paving the way for a more proactive and patient-centric healthcare system.

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