Machine Learning-Assisted Fe–N–C Single-Atom Nanozyme Rapid Screening Platform for Acetylcholinesterase Inhibitors

Traditional screening methods for acetylcholinesterase inhibitors (AChEIs) encounter significant challenges due to two primary factors: subjective errors in colorimetric analysis and reliance on laboratory instruments. To overcome these limitations, this study introduces a rapid screening platform, which integrates a Fe–N–C single-atom nanozyme (SAzyme) biosensor, an anti-interference portable hydrogel kit, and an intelligent analysis platform. The system capitalizes on the remarkable enzyme-like catalytic properties of SAzyme, which effectively transform changes in AChE activity into visible colorimetric signals. In addition, the encapsulation of SAzyme within an agarose acidic hydrogel, combined with the physical light-blocking features of a 3D printing setup, ensures high stability during detection. Moreover, the system is enhanced by a customized smartphone application equipped with machine learning algorithms. This application identifies color signals, processes data, corrects for interference, and provides quantification of AChE activity via the smartphone interface. In practical application, this platform was successfully utilized to screen 10 alkaloids for AChE inhibitory activity, of which only berberine chloride demonstrated a significant inhibitory effect (IC 50 4.61 μM). This study developed a three-unit integrated system (enzyme-like catalytic material, hydrogel kit, and AI algorithm) that overcomes traditional limitations through synergistic innovation, enabling a portable, precise, instrument-free AChEIs screening platform for resource-limited regions.