From empirical exploration to data-driven innovation: The role of artificial intelligence in pharmaceutical taste...

The aversive taste of pharmaceutical formulations, especially bitterness, has been identified as a significant barrier to patient compliance. Taste masking technologies have emerged as significant approaches to improve the acceptability of orally administered drugs. These technologies have evolved from empirical methods to more systematic, precise, and intelligent solutions. Artificial intelligence (AI) advances have given rise to novel opportunities in taste prediction, excipient selection, and formulation optimization. This review critically examines the historical progression of taste masking technologies, elucidates the molecular and physiological basis of taste perception alongside interindividual variability, and compares conventional sensory and analytical techniques with emerging digital and biosensing platforms. Particular emphasis is placed on integrating AI into drug taste assessment, the development of masking strategies, and the control of manufacturing processes. Furthermore, the potential of AI-driven intelligent taste systems to enable precision masking, controlled release, and patient-specific palatability is discussed. This work outlines a conceptual and technological framework for next-generation taste optimization strategies, providing insights into developing smart, efficient, and personalized drug delivery systems.