Quantitative Comparison of Protein Adsorption on Polyethylene Glycol and Zwitterionic Brushes: A Machine Learning Approach

Endowing a material surface with antifouling property using hydrophilic polymer brushes has attracted considerable attention in the design of devices such as water treatment membranes and medical devices. To design an ideal antifouling surface, a comprehensive understanding of protein adsorption on the polymer brush is essential; however, research in this regard is limited because of the complexity of the fouling phenomenon. In this study, a machine learning (ML) approach was employed to evaluate the protein adsorption behavior on a nonionic polyethylene glycol (PEG) brush. The ML model was constructed including the polymer density and molecular weight characteristics of the brush, enabling the prediction of the desired PEG brush state in a specific aqueous environment. The obtained results were then compared with the predictions from the previously constructed data set for a zwitterionic brush, revealing the differences in adsorption properties between the ionic and nonionic brushes. This approach provides a platform for the selection of suitable polymer brushes for practical application in aqueous environments.