The potential of Quantile Regression (QR) and Quantile Support Vector Machine Regression (QSVMR) was analyzed for the definitions of QSAR (Quantitative Structure-Activity Relationship) models associated with a diverse set of chemicals towards a particular endpoint. This study focused on a specific sensitive endpoint (acute toxicity to algae) for which even a narcosis QSAR model is not actually clear. An initial dataset including more than 401 ecotoxicological data for one species of algae (Selenastrum capricornutum) was defined. This set corresponds to a large sample of chemicals ranging from classical organic chemicals to pesticides. From this original data set, the selection of the different subsets was made in terms of the notion of Toxic Ratio (TR), a parameter based on the ratio between predicted and experimental values. The robustness of QR and QSVMR to outliers was clearly observed, thus demonstrating that this approach represents a major interest for QSAR associated with a diverse set of chemicals. We focused particularly on descriptors related to molecular surface properties.