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

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


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.


Neonicotinoids Cowpea aphids QSAR MLR PLS ANN SVM Docking 



Quantitative structure–activity relationship


Inhibitory activity


Organization for Economic Cooperation and Development




Nicotinic acetylcholine receptor

C. Aphids

Cowpea aphids or Aphis craccivora


Multiple linear regression


Partial least squares


Artificial neural networks


Support vector machine


Principal component analysis


Lymnaea stagnalis Acetylcholine Binding Protein


Chemgauss 4


Root-mean-square error


Variable Importance in the Projection


Standard deviation


Root-mean-square deviation


Applicability domain

q 2

Cross-validation correlation coefficient








Mean absolute error


Concordance correlation coefficient










Leverage value


Variance inflation factor


Multi-Criteria Decision Making



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

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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