Herein we present results of a quantitative structure–activity relationship (QSAR) study to identify new antileishmanicidal compounds (Leishmania amazonensis) by using a set of more than 2000 DMs 0D-2D Dragon descriptors and machine learning techniques. A data set of organic chemicals, with antileishmaniasic activity against promastigote forms of the parasite, is used to develop 4 QSAR models based on K nearest neighbors, Support Vector Machine, MultiLayer Perceptron and classification tree techniques. External validation procedures were developed to demonstrate the predictive power of the models. Promastigote´s models correctly classify more than 89 % chemicals in both training and external prediction groups, respectively. In addition to the individual techniques an assembled system of majority voting was personalized with the aim of improving the results of the obtained models. To identify new compounds with potential activity against this parasite databases virtual screening was performed using DrugBank international database. There were identified new potential antileishmaniasic compounds. The current results constitute a step forward in the search for efficient ways to discover new antileishmaniasic lead compounds.
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An approach toward the identification of new antileishmaniasic compounds.
Published:
20 January 2017
by MDPI
in MOL2NET'16, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 2nd ed.
congress CHEMBIOMOL-02: Chem. Biol. & Med. Chem. Workshop, Rostock, Germany-Bilbao, Spain-Galveston, Texas, USA, 2016
Abstract:
Keywords: machine learning, Leishmania, virtual screening
