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.