Abstract
This chapter comprehensively explores the pivotal role of artificial intelligence (AI) in drug discovery and development, encapsulating its potentials, methodologies, real-world applications, and inherent challenges. Starting with an in-depth introduction to AI, including its subfields such as machine learning (ML), deep earning (DL), natural language processing (NLP), etc., the chapter progresses to explain various AI algorithms such as regression, support vector machines, neural networks, etc. It further delves into the methodologies for optimizing and validating AI models, detailing the metrics used for quantitative assessment. The chapter then shifts focus to the drug discovery and development process, illuminating AI’s transformative influence across all stages of the drug discovery process and spotlighting real-world implementations through AI-native drug discovery companies and their innovative platforms. The chapter also explored the real-world challenges of AI application in drug discovery, such as data availability, ethical concerns, and the harmonization of AI with traditional methods, alongside potential solutions like data augmentation and explainable AI (XAI). Regulatory perspectives, focusing on the United States Food and Drug Administration’s viewpoint, illuminate the evolving intersection of AI and regulatory science. Concluding with a forward-looking perspective on the future of AI in drug discovery, this chapter serves as an invaluable resource for anyone interested in understanding the ongoing revolution of AI in this field.
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References and Further Reading
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Mak, KK., Wong, YH., Pichika, M.R. (2023). Artificial Intelligence in Drug Discovery and Development. In: Hock, F.J., Pugsley, M.K. (eds) Drug Discovery and Evaluation: Safety and Pharmacokinetic Assays. Springer, Cham. https://doi.org/10.1007/978-3-030-73317-9_92-1
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