Transforming drug discovery through the fusion of AI-driven analysis and protein micropatterning

Traditional drug discovery is hampered by high costs, long timelines, and low success rates due to inefficient screening and inadequate model systems. The convergence of artificial intelligence (AI) and functional protein micropatterning offers a novel paradigm to address these limitations by accelerating candidate identification and enhancing physiological relevance.

The purpose of this critical perspective is to provide the reader with the author's expert opinion on the convergence of AI and micropatterning, synthesizing current evidence and discussing future opportunities and limitations. The Web-of-Science and PubMed databases were used to collate information. Within this article, coverage is given to the recent advances in combining machine learning and deep learning for efficient virtual screening, molecular design, and structural prediction with state-of-the-art protein micropatterning techniques that generate standardized, biomimetic assay platforms.

The integration of automated imaging and AI-driven data analysis enables high-throughput, information-rich experimental workflows. Persistent challenges include explainable AI requirements, data quality, and evolving regulatory frameworks supporting non-animal models. These AI-enabled functional micropatterning platforms offer significant benefits for drug discovery. Over the next decade, advances in explainable AI and workflow automation will be essential for widespread adoption, regulatory acceptance, and the realization of closed-loop systems (AI-driven experimental iteration) that reshape pharmaceutical research by enabling close collaboration between scientists and intelligent technologies.