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Topological QSAR Modelling of Carboxamides Repellent Activity to Aedes Aegypti
Author(s) -
Doucet J. P.,
DoucetPanaye A.,
Papa E.
Publication year - 2019
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201900029
Subject(s) - quantitative structure–activity relationship , aedes aegypti , context (archaeology) , insect repellent , deet , support vector machine , machine learning , artificial intelligence , computer science , toxicology , biological system , computational biology , biochemical engineering , biology , ecology , engineering , paleontology , larva
Abstract Aedes aegypti vector control is of paramount importance owing to the damages induced by the various severe diseases that these insects may transmit, and the increasing risk of important outbreaks of these pathologies. Search for new chemicals efficient against Aedes aegypti , and devoid of side‐effects, which have been associated to the currently most used active substance i. e. N,N‐diethyl‐ m ‐toluamide (DEET), is therefore an important issue. In this context, we developed various Quantitative Structure Activity Relationship (QSAR) models to predict the repellent activity against Aedes aegypti of 43 carboxamides, by using Multiple Linear Regression (MLR) and various machine learning approaches. The easy computation of the four topological descriptors selected in this study, compared to the CODESSA descriptors used in the literature, and the predictive ability of the here proposed MLR and machine learning models developed using the software QSARINS and R, make the here proposed QSARs attractive. As demonstrated in this study, these models can be applied at the screening level, to guide the design of new alternatives to DEET.