Predicting acute contact toxicity of pesticides in honeybees (Apis mellifera) through a k-nearest neighbor model

F. Como, E. Carnesecchi, S. Volani, J. L. Dorne, J. Richardson, A. Bassan, M. Pavan, E. Benfenati

Research output: Contribution to journalArticlepeer-review


Ecological risk assessment of plant protection products (PPPs) requires an understanding of both the toxicity and the extent of exposure to assess risks for a range of taxa of ecological importance including target and non-target species. Non-target species such as honey bees (Apis mellifera), solitary bees and bumble bees are of utmost importance because of their vital ecological services as pollinators of wild plants and crops. To improve risk assessment of PPPs in bee species, computational models predicting the acute and chronic toxicity of a range of PPPs and contaminants can play a major role in providing structural and physico-chemical properties for the prioritisation of compounds of concern and future risk assessments. Over the last three decades, scientific advisory bodies and the research community have developed toxicological databases and quantitative structure-activity relationship (QSAR) models that are proving invaluable to predict toxicity using historical data and reduce animal testing. This paper describes the development and validation of a k-Nearest Neighbor (k-NN) model using in-house software for the prediction of acute contact toxicity of pesticides on honey bees. Acute contact toxicity data were collected from different sources for 256 pesticides, which were divided into training and test sets. The k-NN models were validated with good prediction, with an accuracy of 70% for all compounds and of 65% for highly toxic compounds, suggesting that they might reliably predict the toxicity of structurally diverse pesticides and could be used to screen and prioritise new pesticides.

Original languageEnglish
Pages (from-to)438-444
Number of pages7
Publication statusPublished - Jan 1 2017


  • Acute contact toxicity
  • Honey bees
  • In silico models
  • k-NN
  • Pesticides

ASJC Scopus subject areas

  • Environmental Chemistry
  • Chemistry(all)


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