Development of QSAR Models on the Fouling-Release Performance of Silicone Oil-modified Siloxane Polyurethane Coatings

Polymer coatings with a fouling release activity is an effective method to prevent marine biological fouling caused by large number of marine organisms (over 4000) and ensure stability and safety of marine facilities. In this study, a novel cheminformatics-based approach was employed to investigate the fouling-release performance of siloxane-PU coating system. The structure-fouling release activity relationship was modeled for a set of 18 siloxane PU coatings by incorporation phenylmethyl silicones oils (PMM-1025, PMM-1043, PMM-5021, PMM-6025, PMM-0021, PMM-0025 of 1, 2 and 5 wt%). A specific structural descriptors encoding approach was applied for these systems, based on a mixture-based encoding, to feed the complex polymeric systems in machine learning algorithms. Several predictive quantitative structure-fouling release relationships models were developed using machine learning techniques, from multiple linear regression to nonlinear random forest methods, followed by scoring them based on high performance accuracy and validation with rigorous internal fit R2 train values between 0.73 to 0.95 and external predictivity R2ext between 0.68 to 0.88. Random Forest method was the best nonlinear one for predicting diatom removal activity. This work indicates that both linear and nonlinear machine learning-based modeling can be beneficial to predict fouling release properties of polymer coatings with an effective fouling release performance.