Machine learning-assisted broad-spectrum aptamer discovery and intelligent detection of neonicotinoid pesticides.

A machine learning-assisted computational strategy was developed for the de novo discovery of broad-spectrum aptamers targeting neonicotinoid pesticides (NEOs). Randomly generated DNA sequences were first classified according to secondary-structure similarity using t-distributed stochastic neighbor embedding (t-SNE) and K-means clustering, providing a structurally diverse initial library. Molecular docking and binding-pocket overlap analysis were then used to identify conserved interaction regions across multiple NEOs, followed by directed single-nucleotide mutagenesis and iterative computational screening to optimize broad-spectrum recognition. The resulting aptamer, NEO 3_4, exhibited improved binding toward multiple NEOs, with a dissociation constant (Kd) of 1.48 μM. An MIL-88(Fe)-NH2@PtIr nanozyme-assisted paper-based colorimetric sensing platform was subsequently developed using NEO 3_4. The platform achieved a linear detection range of 0.05-100 nM and a limit of detection of 26.68 pM. Combined with smartphone-based RGB extraction and artificial neural network analysis, the method enabled accurate quantification of NEOs in vegetable samples, with recoveries of 98.95-102.75% and RSDs below 3.56%. This study provides a machine learning-assisted strategy for de novo broad-spectrum aptamer discovery and a portable sensing platform for NEO monitoring.