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Neonicotinoid insecticide design: molecular docking, multiple chemometric approaches, and toxicity relationship with Cowpea aphids

  • Alina Bora
  • Takahiro Suzuki
  • Simona Funar-TimofeiEmail author
Research Article

Abstract

Neonicotinoids are the fastest-growing class of insecticides successfully applied in plant protection, human and animal health care. The significant resistance increases led to the urgent need for alternative new neonicotinoids, with improved insecticidal activity. We performed molecular docking to describe a common binding mode of neonicotinoids into the nicotinic acetylcholine receptor, and to select the appropriate conformations to derive models. These were further used in a QSAR study employing both linear and nonlinear approaches to model the inhibitory activity against the Cowpea aphids. Linear modeling was performed by multiple linear regression and partial least squares and nonlinear modeling by artificial neural networks and support vector machine methods. The OECD principles were considered for QSAR models validation. Robust models with predictive power were found for neonicotinoid diverse structures. Based on our QSAR and docking outcomes, five new insecticides were predicted, according to the model applicability domain, the ligand efficiencies, and the binding mode. Therefore, the developed models can be confidently used for the prediction of the insecticidal activity of new chemicals, saving a substantial amount of time and money and, also, contributing to the chemical risk assessment.

Keywords

Neonicotinoids Cowpea aphids QSAR MLR PLS ANN SVM Docking 

Abbreviations

QSAR

Quantitative structure–activity relationship

LC50

Inhibitory activity

OECD

Organization for Economic Cooperation and Development

IMI

Imidacloprid

nAChR

Nicotinic acetylcholine receptor

C. Aphids

Cowpea aphids or Aphis craccivora

MLR

Multiple linear regression

PLS

Partial least squares

ANNs

Artificial neural networks

SVM

Support vector machine

PCA

Principal component analysis

Ls-AChBP

Lymnaea stagnalis Acetylcholine Binding Protein

CG4

Chemgauss 4

RMSE

Root-mean-square error

VIP

Variable Importance in the Projection

SD

Standard deviation

RMSD

Root-mean-square deviation

AD

Applicability domain

q 2

Cross-validation correlation coefficient

LOO

Leave-one-out

L7O

Leave-seven-out

LMO

Leave-more-out

MAE

Mean absolute error

CCC

Concordance correlation coefficient

tr

Training

ext

External

cv

Cross-validation

scr

Scrambled

h

Leverage value

VIF

Variance inflation factor

MCDM

Multi-Criteria Decision Making

Notes

Acknowledgments

Access to the Chemaxon Ltd., OpenEye Ltd., and QSARINS (from Prof. Paola Gramatica from the University of Insubria, Varese, Italy) software is greatly acknowledged by the authors.

Funding information

This work was financially supported by the Project No. 1.1/2017 of the Institute of Chemistry Timişoara of Romanian Academy.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

11356_2019_4662_MOESM1_ESM.PDF
ESM 1 (PDF 1815 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Chemistry Timisoara of the Romanian AcademyTimisoaraRomania
  2. 2.Natural Science LaboratoryToyo UniversityTokyoJapan

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