Computationally guided design of molecularly imprinted polymer interfaces for electrochemical chemosensing: From...

Molecularly imprinted polymers (MIPs) are widely used as robust synthetic receptors for electrochemical chemosensing, but a limited understanding of the relationships among molecular recognition, polymer architecture, and analytical performance constrains the rational design of high-performance MIP-based sensors. This review critically examines computation-guided strategies for designing MIP receptors, imprinted layers, and electrode-integrated sensing interfaces. In particular, we emphasize quantum-chemical calculations, molecular docking, molecular dynamics simulations, chemometrics, and data-driven approaches for functional monomer selection, formulation optimization, recognition-site analysis, interferent assessment, and analyte-binding interpretation. We discuss how computational descriptors, including interaction energies, binding geometries, hydrogen-bonding patterns, electrostatic complementarity, and dynamic stability, can provide molecular-level insight into recognition processes and their impact on analytical performance, including selectivity, imprinting factor, response time, recovery, reproducibility, and limit of detection. At the same time, we highlight the current limitations of translating models of pre-polymerization complexes into device-level performance, particularly when key factors, including electrode architecture, film morphology, template removal, nonspecific adsorption, mass transport, and matrix effects, are not explicitly considered. By connecting molecular-level modeling with polymer formulation, electrode-MIP interface engineering, and analytical validation, this review outlines the transition from empirical MIP sensor development toward more predictive and application-oriented electrochemical chemosensor design. Future progress will require multiscale modeling, explicit treatment of electrode-polymer-electrolyte interfaces, improved reporting standards, curated datasets, and machine-learning-assisted optimization.