Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening

Two-dimensional bond-based bilinear indices and linear discriminant analysis are used in this report to perform a quantitative structureeactivity relationship study to identify new trypanosomicidal compounds. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop the theoretical models. Two discriminant models, computed using bondbased bilinear indices, are developed and both show accuracies higher than 86% for training and test sets. The stochastic model correctly indentifies nine out of eleven compounds of a set of organic chemicals obtained from our synthetic collaborators. The in vitro antitrypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi is assayed. Both models show a good agreement between theoretical predictions and experimental results. Three compounds showed IC₅₀ values for epimastigote elimination (AE) lower than 50 μM, while for the benznidazole the IC₅₀ = 54.7 μM which was used as reference compound. The value of IC₅₀ for cytotoxicity of these compounds is at least 5 times greater than their value of IC₅₀ for AE. Finally, we can say that, the present algorithm constitutes a step forward in the search for efficient ways of discovering new antitrypanosomal compounds.