The Convergence of Artificial Intelligence and Microfluidics in Drug Research and Development

Drug research and development (R&D) plays a crucial role in supporting public health. However, the traditional drug-discovery paradigm is hindered by significant drawbacks, including high costs, lengthy development timelines, high failure rates, and limited output of new drugs. Recent advances in micro/nanotechnology, along with progress in computer science, have positioned microfluidics and artificial intelligence (AI) as promising transformative tools for drug development. Microfluidics offers miniaturized, multiplexed, and versatile platforms for high-dimensional data acquisition, while AI enables the rapid processing of complex, large-scale microfluidic data; together, they are accelerating a paradigm shift in the drug-discovery process. This paper first outlines the mainstream microfluidic strategies and AI models used in drug R&D. It then summarizes and discusses real-world applications of the integrated use of these technologies across various stages of drug discovery, including early drug discovery, drug screening, drug evaluation, drug manufacturing, and drug delivery systems. Finally, the paper examines the main limitations of microfluidics and AI in drug R&D and offers an outlook on the future convergence of these technologies.