This work consists on a new chemoinformatic approach based on two complementary artificial intelligence concepts. Random Forest and Kohonen neural network are applied on this context. The former provides a relevance measure of the numerical descriptors encoding either an ionic liquid or its mixtures. The code of a given chemical system is weighted according that relevance measure. The Kohonen neural network is trained with a set of weighted chemical systems. The next step comprises the use of the trained neural network as platform to obtain a tuned profile of numerical descriptors representing a generical chemical system. The tuning mechanism involves the topology of a chemical system‐encoding vector in the neural network. The last step comprises the use of the tuned chemical systems to build predictive models of viscosities. The MOLMAP encoding technology is applied to represent ionic liquid systems and its mixtures.