Neres_2025_J.Comput.Chem_46_e70139

Given the rising incidence of prostate cancer (PCa), there is an increasing demand for cost-effective and reliable methods for early detection using the prostate-specific antigen (PSA) biomarker. PCa remains a leading cause of mortality among individuals with prostates aged 55-80 years. Molecularly Imprinted Polymers (MIPs) represent a promising solution due to their selectivity, sensitivity, and stability for PSA detection. However, the synthesis of MIPs for protein targets presents significant challenges, particularly in the rational selection of functional monomers and cross-linkers. This study introduces a theoretical framework to aid the development of MIPs by assisting in the selection of optimal reagents for PSA targeting. A novel algorithm, the Molecular Binding Algorithm for Surface Mapping (MBASM), was developed to efficiently generate amino acid-monomer complexes. The integrated MBASM + DFT approach was validated through comparison with the GFN2-xTB method and the Quantum Cluster Growth approach implemented in the CREST program. The results demonstrated strong agreement between the methods, establishing MBASM + DFT as a viable and innovative alternative tool for predicting interaction structures and energies. Through this strategy, promising monomers for PSA-targeted MIP synthesis were identified, including itaconic acid, 4-imidazole acrylic acid, and methacrylic acid, with 1,4-divinylbenzene emerging as the most effective cross-linker. This computational methodology provides a powerful and systematic approach for optimizing MIP synthesis aimed at selective PSA detection.