## 5 Innovative Approaches for Enzyme Immobilization
### AI-Based Enzyme Immobilization
[251] recently introduced ML to forecast critical enzyme-MOF composite properties, including enzyme loading, immobilization yield, activity retention, and reusability. Accurate prediction of these factors can streamline MOF substrate synthesis for enzyme immobilization, enhancing biocatalytic efficiency and reducing costs. The study also evaluated two ML models, the random forest method (RFM) and Gaussian process regression (GPR), demonstrating their ability to generalize predictions based on MOF metal/ligand properties and enzyme features. The sensitivity analysis identified the most influential parameters. The optimized model is then applied to predict outcomes for new experimental biocatalysis data outside the original training set. ML also optimizes multienzyme configurations. For instance, [PERSON] et al. improved the combination of lipase, amylase, and endoglucanase (LIP-AMY-END) using an ANN with the Levenberg-Marquardt (LM) algorithm. This approach boosted endoglucanase activity by five-fold and enhanced deinking efficiency by 27.8% at 50 \({}^{\circ}\)C and 11% at 80 \({}^{\circ}\)C [259; 260], respectively. The enzymatic responses of Cnida antartica lipase B (calB) in porous acrylic resin were also investigated by [PERSON] et al. using a wavelet neural network (WNN) model [261]. The conversion rates were mostly influenced by the substrate molar ratio, and AI predictions highlighted how crucial it is to optimize the reaction parameters. In order to ensure maximum reaction efficiency, AI assisted in preventing the loss of immobilized enzyme activity. Figure 8: The application of machine learning algorithms to estimate the enzyme immobilization performance metrics of enzyme-nanocomposite biocatalysts. The first ML-guided method for optimizing enzyme/zeolitic imidazolate framework (ZIF) synthesis for enhanced performance was most recently presented by [PERSON] et al. [262]. Their iterative ML workflow produced GOx/ZIF(G151) and HRP/ZIF (G150) composites, with overall performance indices (OPI = encapsulation efficiency \(\times\) activity \(\times\) stability) at least 1.3 times higher than prior benchmarks, using glucose oxidase (GOx) and horseradish peroxidase (HRP) as model enzymes and Zn (eIM)\({}_{2}\) as the ZIF. The trained random forest model's advanced statistical analysis also revealed important synthesis-structure performance correlations, offering vital information for next enzyme/ZIF research. For enzyme immobilization, factor material selection, activation conditions, and reactor design significantly impact performance [263]. AI can optimize these parameters, enabling the design of more efficient immobilized enzymes. Additionally, AI can predict and adjust support materials or reaction conditions to maintain enzyme stability across cycles, minimizing activity loss [264]. Since immobilized enzymes often require different catalytic conditions, AI can also help predict optimal productivity and yield, advancing sustainable biocatalyst for biotech applications. Overall, ML has significantly enhanced enzyme stability, effectiveness, and reusability in immobilization processes. However, challenges involving data quality, interpretability, and material limitations persist. Researchers are exploring various ML methods, including support vector machines (SVMs) and deep learning, to further improve immobilization techniques, while addressing concerns about generalization and algorithmic biases [249].