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AI-integrated microfluidics for drug screening: From single cell to organ-on-a-chip
Hongyun Yin1, Zheyu Li1, Zinuo Shen2
1School of Laboratory Medicine, Hubei Shizhen Laboratory, Hubei Provincial Hospital of Traditional Chinese Medicine, Hubei University of Chinese Medicine, Wuhan 430065, China.
Acta Pharmaceutica Sinica. B
|March 30, 2026
Summary
Artificial intelligence (AI) and microfluidics accelerate drug discovery by improving screening efficiency. This review details AI-assisted microfluidic platforms, from single cells to organs-on-chips, for precision medicine.
Area of Science:
- Biomedical Engineering
- Pharmacology
- Artificial Intelligence
Background:
- Drug discovery is slow and expensive, with screening inefficiencies being a major bottleneck.
- Microfluidic technology offers a solution for in vitro drug screening by mimicking cellular environments.
- Integrating artificial intelligence (AI) with microfluidics enhances analysis and control for drug screening.
Purpose of the Study:
- To review advancements in AI-assisted microfluidic drug screening.
- To organize findings by increasing biological complexity (1D, 2D, 3D, 3D+).
- To discuss challenges and future directions in AI-enhanced microfluidics for drug discovery.
Main Methods:
- Systematic review of AI-assisted microfluidic drug screening technologies.
- Categorization of platforms by biological complexity: single-cell (1D), multicellular arrays (2D), spheroids (3D), and Organ-on-a-chip (3D+).
- Analysis of AI's role in enhancing throughput, sensitivity, and physiological relevance.
Main Results:
- AI algorithms significantly improve screening throughput, sensitivity, and physiological relevance across various microfluidic scales.
- Advancements range from single-cell analysis to complex Organ-on-a-chip models.
- AI facilitates automated data analysis, pattern recognition, and intelligent experimental control.
Conclusions:
- AI-assisted microfluidics represent a pivotal advancement in accelerating drug screening.
- Overcoming challenges in data, model robustness, interpretability, and integration is crucial.
- Future directions point towards AI-enhanced microfluidics driving precision drug discovery and biomedical research.
