Machine Learningfor Quantitative Prediction of ProteinAdsorption on Well-Defined Polymer Brush Surfaces with Diverse ChemicalProperties

Polymer informatics has attracted increasing attention because machine learning can establish quantitative structure–property relationships in polymer materials. Understanding and controlling protein adsorption on polymer surfaces are crucial for various applications, such as protein immobilization supports, biosensors, and antibiofouling surfaces. However, protein adsorption is a complex phenomenon that is difficult to predict quantitatively owing to the involvement of multiple factors. Therefore, this study aims to establish a machine learning model for protein adsorption on densely packed polymer brushes with various chemical structures, as these surfaces are well-suited for analyzing structure–property correlations between the polymer’s chemical structure and adsorption amount during initial protein adsorption. Two proteins, bovine serum albumin (BSA) and lysozyme, are adopted as target proteins, with the expectation that differences in their charge profiles will be reflected in the resulting machine learning model. The descriptors of the polymer brush surfaces include their grafted structures (thickness) and chemical properties, which are described by the contact angle and ζ potential. This allows physicochemical knowledge to be incorporated into the machine learning model. Random forest exhibits the best performance in all situations, accurately predicting the amounts of adsorbed BSA and lysozyme. In addition, the prediction of the contact angle and ζ potential by machine learning also enables a quantitative and explainable prediction of protein adsorption based on theoretical molecular descriptors, ensuring that no characteristics are overlooked. Moreover, the model is used to analyze the contributions of electrostatic and hydrophobic interactions to protein adsorption. In conclusion, a machine learning model is developed to predict protein adsorption on polymer brush surfaces, incorporating descriptors such as the grafted structure, contact angle, and ζ potential. It provides quantitative predictions and analyzes the roles of electrostatic and hydrophobic interactions, advancing the design of functional polymer surfaces for applications in biosensors and antifouling technologies.