QSAR and docking studies of novel antileishmanial diaryl sulfides and sulfonamides

Abstract

Leishmaniasis is a neglected disease transmitted in many tropical and sub-tropical countries, with few studies devoted to its treatment. In this work, the activities of two antileishmanial compound classes were modeled using Dragon descriptors, and multiple linear (MLR) and support vector machines (SVM) as linear and nonlinear regression methods, respectively. Both models were highly predictive, with calibration, leave-one-out validation and external validation R(2) of 0.79, 0.72 and 0.78, respectively, for the MLR-based model, improving significantly to 0.98, 0.93 and 0.90 when using SVM modeling. Therefore, novel compounds were proposed using the QSAR models built by combining the substructures of the main active compounds of both classes. The most promising structures were docked into the active site of Leishmania donovani α,β tubulin (Ld-Tub), demonstrating the high affinity of some new structures when compared to existing antileishmanial compounds.