Many ionic liquids are soluble in water and their impact on the aquatic environment has to be evaluated. However, due to the large number of ionic liquids and lack of experimental data, it is necessary to develop estimation procedures in order to reduce the materials and time consumption. In this study using multilayer perceptron neural network (MLP), ant colony optimization (ACO) and multiple linear regression (MLR) strategies, good predictive quantitative structure–activity relationships (QSAR) models were introduced and structural parameters affecting ecotoxicity of ionic liquids in limnic green algae (Scenedesmus vacuolatus) were revealed. Moreover, principal component analysis (PCA) and cluster analysis (CA) approaches were also applied to visualize any possible patterns or relationships among ionic liquids data. It was revealed that selected descriptors of the MLR model are also capable of clustering ionic liquids according to their four level of toxicity
