An Update of Sustainable Molecular Imprinting Polymer Through Machine Learning and Quantum Chemical Approaches for Green Polymer Design

Molecularly imprinted polymers (MIPs) are synthetic materials with specific recognition sites formed by polymerizing monomers around a target molecule. MIPs are widely utilized across various fields due to high selectivity, affinity, reusability, durability, sensitivity, and reproducibility. Environmental‐friendly MIP development requires innovative strategies to minimizing environmental impact while enhancing performance, and this could be achieved using computational methods by allowing rational MIP design without extensive experimental trial and error. Quantum chemical calculations (QCCs) as a computational approach have contributed to the rational design of MIPs. Combining QCC with machine learning (ML) makes it possible to screen rational monomers, solvents, and synthesis conditions under the concept of green chemistry. ML models with density functional theory (DFT)–based descriptors and experimental data estimate optimal polymer compositions and parameters, reducing trial and error. The integration of DFT‐derived descriptors with ML may facilitate the prioritization of monomers, solvents, compositions, and synthesis conditions before experimental validation, thereby potentially reducing unnecessary experiments and associated waste generation. Remaining issues include heterogeneous data, computational complexity, and dynamic environment modeling. Further developments will depend on open‐source databases, automated pipelines, and artificial intelligence (AI) platforms integrating computation with experimentation. These will lead to easier MIP design and allow for applications in biosensing, environmental remediation, and diagnostics, establishing MIPs as an essential tool for addressing global health and sustainability challenges.