Department of Anesthesiology, The First Hospital of Jilin University, 71 Xinmin Street, Changchun 130012, China.
Department of Endocrinology, Key Laboratory of Endocrinology of National Health Commission, Translation Medicine Centre, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, China.
ACS Appl Mater Interfaces. 2024 Jul 31;16(30):38832-38851. doi: 10.1021/acsami.4c07665. Epub 2024 Jul 17.
Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.
表型药物发现(PDD),即直接利用生物系统来发现有效药物,近年来重新兴起。在过去的几年中,人工智能(AI)的快速发展为在微流控平台上增强表型药物筛选提供了许多机会,利用其预测能力、数据分析、高效的数据处理等。微流控与 AI 的结合有望彻底改变表型药物发现的格局。通过将先进的微流控平台与 AI 算法相结合,研究人员可以快速筛选大量化合物库,鉴定新的药物候选物,并以空前的速度和效率阐明复杂的生物学途径。本综述介绍了基于 AI 的微流控技术的最新进展和挑战及其在药物发现中的应用。我们讨论了用于高通量筛选的微流控系统与 AI 驱动的分析相结合,用于表型特征描述、药物-靶标相互作用和预测建模。此外,我们强调了 AI 驱动的微流控在实现自动化药物筛选系统方面的潜力。总体而言,AI 驱动的微流控技术通过快速、经济高效且准确地鉴定具有治疗相关性的化合物,代表了一种有前途的表型药物发现方法。