An Approach to Identify New Insecticides Against Myzus Persicae. In silico Study Based on Linear and Non‐linear Regression Techniques

Abstract

This paper is dedicated to Prof. Paola Gramatica on the occasion of her retirement.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 insectici-dal 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.

Application of Molecular Docking, Homology Modeling, and Chemometric Approaches to Neonicotinoid Toxicity against Aphis craccivora

“…In this context, theoretical methods such as linear and nonlinear regression methods, QSAR (Quantitative Structure‐Activity Relationships), homology modeling, molecular docking, 2D/3D similarity search, pharmacophore modeling, etc. applied in cheminformatics to design new medicines, have also been successfully used to predict novel pesticides with less adverse effects, to fill experimentally data gaps and to reduce or replace toxicity testing on animals [25–30] . Most of these theoretical studies related to the toxicity of A. craccivora and A. mellifera have used a relatively small number of compounds, and fewer scaffolds.…”

“…applied in cheminformatics to design new medicines, have also been successfully used to predict novel pesticides with less adverse effects, to fill experimentally data gaps and to reduce or replace toxicity testing on animals. [25][26][27][28][29][30] Most of these theoretical studies related to the toxicity of A. craccivora and A. mellifera have used a relatively small number of compounds, and fewer scaffolds. For instance, Zhao and Li [31] used 30 neonicotinoids chosen from a dataset of 50 compounds in a 3D-QSAR, homology, and docking study.…”