The Quantitative Structure-Activity Relationship (QSAR) approach for predicting the biological activity and physicochemical properties of mixtures is gaining prominence, driven by the growing demand for highly engineered materials designed for specific functions. Developing mixture descriptors that effectively capture the intricacies of multi-component materials presents a significant challenge due to their structural complexity. We implemented a series of existing and new mixing rules to drive the mixture descriptors and develop mixture-based-QSAR (mxb-QSAR) models. We evaluated 12 additive mixture descriptors, and a novel non-additive combinatorial descriptor derived from the Cartesian product. These descriptors were used to model the fouling release (FR) property of 18 silicone oil-infused PDMS coating polymers by characterizing the removal of Ulva. linza. Various linear and nonlinear mxb-QSAR models were obtained using these 13 mixture descriptors. The best model, derived from the newly proposed Cartesian-based combinatorial mixture descriptors, employed a decision tree in combination with a two-stage feature importance feature selection. This model achieved a coefficient of determination R² of 0.987 for both training and test sets, along with a cross-validation Q² _LOO of 0.791. The success of the nonlinear model and combinatorial descriptors underscores the significance of complex relationships among variables, as well as the synergistic effects of the components on fouling release properties.