Predicting antiprotozoal activity of benzyl phenyl ether diamine derivatives through QSAR multi-target and molecular topology.

Multi-target QSAR is a novel approach that can predict simultaneously the activity of a given chemical compound on different pharmacological targets. In this work, we have used molecular topology and statistical tools such as multilinear regression analysis and artificial neural networks, to achieve a multi-target QSAR model capable to predict the antiprotozoal activity of a group of benzyl phenyl ether diamine derivatives. The activity was related to three parasites with a high prevalence rate in humans: Trypanosoma brucei rhodesiense, Plasmodium falciparum, and Leishmania donovani. The multi-target model showed a high regression coefficient (R(2) = 0.9644 and R(2) = 0.9235 for training and test sets, respectively) and a low standard error of estimate (SEE = 0.279). Model validation was performed with an external test (R(2) = 0.9001) and a randomization analysis. Finally, the model was applied to the search of potential new active compounds.