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An Approach to Identify New Insecticides Against Myzus Persicae. In silico Study Based on Linear and Non‐linear Regression Techniques
Author(s) -
Crisan Luminita,
Borota Ana,
Suzuki Takahiro,
FunarTimofei Simona
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.201800119
Subject(s) - myzus persicae , support vector machine , linear regression , in silico , partial least squares regression , biological system , loo , pharmacophore , quantitative structure–activity relationship , machine learning , artificial intelligence , toxicology , mathematics , computer science , biology , chemistry , stereochemistry , botany , biochemistry , aphid , gene
Abstract Neonicotinoids are known to have high insecticidal potency, low mammalian toxicity and relatively tough activity for the development of resistance against aphids. A series of guadipyr insecticides, active against Myzus persicae was engaged in silico studies, based on Multiple Linear Regression (MLR), Partial Least Squares regression (PLS), Artificial Neural Networks (ANN), Support Vector Machine (SVM) and Pharmacophore modeling. Robust and predictive models were built using correlations between the insecticidal profile, expressed by experimental pLC 50 values, and molecular descriptors, calculated from the energy optimized structures. Four new potential insecticides active against Myzus persicae and their predicted pLC 50 toxicity values were reported for the first time. The models presented here can be used as an approach in the screening and prioritization of chemicals in a scientific and regulatory frame and for toxicity prediction.