Computationally guided design of molecularly imprinted polymers for electrochemical biosensing interfaces

Rapid, accurate, and on-site detection of foodborne pathogens remains a critical challenge in ensuring food safety. Traditional microbiological methods, including culture-based assays, ELISA, and PCR, often require long processing times, specialized equipment, and trained personnel, limiting their practical application in real-time monitoring. Electrochemical biosensors have emerged as promising alternatives due to their portability, fast response, low cost, and compatibility with complex food matrices. Molecularly imprinted polymers (MIPs) offer a robust synthetic recognition layer for these sensors, providing high chemical stability and tenable selectivity while overcoming limitations of biological recognition elements such as antibodies and enzymes. However, conventional MIP fabrication often relies on empirical trial-and-error approaches, which can reduce reproducibility and slow sensor development. This review summarizes recent advances in computationally guided MIP design for electrochemical biosensing of key foodborne pathogens, including Salmonella spp., Listeria monocytogenes, Escherichia coli O157:H7, Staphylococcus aureus, and Pseudomonas aeruginosa. We discuss how molecular docking, density functional theory, and molecular dynamics simulations can predict template-monomer interactions, optimize cavity formation, and guide polymer properties, thereby improving sensor sensitivity, selectivity, stability, and response time. By integrating computational modeling with experimental electrochemical methods, these approaches enable a mechanism-driven development of portable, high-performance biosensors for microbial detection. This convergence of computational and microbiological methods provides a pathway toward next-generation, ready-to-use devices for rapid foodborne pathogen monitoring.