Artificial intelligence-assisted design of molecularly imprinted polymers for enriching C-reactive protein

Official URL: https://dx.doi.org/10.1016/j.matchemphys.2025.131849

The rational design of high-affinity biomimetic materials is often hampered by the high cost of experimental research. This study presents the development of artificial intelligence-assisted molecularly imprinted polymer (AI-MIP) nanostructures for the selective recognition of C-Reactive Protein (CRP), a clinically significant biomarker associated with inflammation and infection. The development of high-purity CRP isolation systems is clinically important for accurate diagnosis, reliable disease monitoring, and effective treatment planning. To address these challenges, CRP-imprinted nanostructures were synthesized using a surfactant-free emulsion polymerization method, which ensured clean surface properties and high molecular specificity. Methyl methacrylate (MMA) and CRP were used in the formation of a pre-polymerization complex, and subsequently, 2-hydroxyethyl methacrylate (HEMA) and silanized reduced graphene oxide (S-rGO) were incorporated to obtain the final polymeric nanomaterial. After polymerization, CRP was successfully removed from the polymer matrix, yielding the CRP-imprinted poly[HEMA-co-MMA-co-(S-rGO)] structure. CRP binding studies confirmed the selective recognition of CRP by the imprinted nanostructure. CRP-imprinted nanostructure exhibited the highest binding affinity at a CRP concentration of 1 mg/mL, reaching a maximum binding capacity, Qmax, of 350 mg/g. Characterization studies, including FTIR, SEM, and zeta size and potential analysis (317 nm,–0.213 mV, respectively), validated the successful formation, structural integrity, and colloidal stability of the synthesized nanostructure. Unlike conventional recognition elements such as antibodies or aptamers, molecularly imprinted polymers (MIPs) offer a cost-effective and chemically stable alternative for CRP isolation and purification, demonstrating selectivity and specificity toward the target molecule. To reduce the experimental burden in terms of cost, time, and resource consumption, an experimentally constrained interpolation-based data augmentation strategy was implemented, enabling the construction of high-fidelity regression models from a minimal number of laboratory trials. Among the evaluated algorithms, CatBoost Regressor achieved superior performance, accurately predicting binding behavior under varying experimental conditions. Feature importance analysis highlighted reaction time as the most influential parameter affecting CRP binding performance. This study represents, to our knowledge, the first demonstration of a CRP-selective MIP designed through integrated AI and in-silico data augmentation techniques, offering a practical and scalable pathway for affinity material discovery. The approach not only accelerates material optimization but also significantly reduces the reliance on costly trial-and-error procedures.