A Synergistic Experimental and Computational Approach to the Design of MIPs Targeting Metronidazole

Abstract Metronidazole (MTZ) is a widely used nitroimidazole antibiotic, and its persistence has raised growing concerns as an emerging environmental contaminant. This study focuses on the design of an electrochemical sensor based on Molecularly Imprinted Polymers (MIPs) for its detection, by a synergistic computational-experimental strategy. A hierarchical computational framework combining conformational sampling, density functional theory and finite-temperature condensed-phase ab initio molecular dynamics was employed to evaluate aniline (ANL) and dopamine (DA) as candidate monomers. Static calculations provided a preliminary screening based on monomer-template complex stability, while molecular dynamics simulations assessed the dynamic balance between potential template affinity and reversibility. Although DA exhibited more persistent hydrogen-bonding and aromatic interactions, ANL showed a more favourable interaction profile, promoting efficient MTZ removal and accessible recognition cavities. These findings support the selection of ANL as the optimal functional monomer. The ANL-based MIP was therefore electropolymerized on screen-printed carbon electrodes (SPCEs) via cyclic voltammetry (CV) and characterized via differential pulse voltammetry (DPV) using K3Fe(CN)6, yielding an imprinting factor (IF) of 2.6, a linear response in the range of 27−129 nmol L−1, a sensitivity of 26.01 A L mol−1, and limits of detection and quantification of 6.7 and 22.3 nmol L−1, respectively. The sensor also showed good repeatability, reproducibility, and negligible interference from other antibiotics. Its superior analytical performance relative to the corresponding DA-based MIP experimentally confirms the computational prediction. These findings demonstrate that combining atomistic pre-screening with targeted experimental validation can reduce empirical trial-and-error and accelerate the rational development of selective MIP-based sensors.