Classification models for identifying substances exhibiting acute contact toxicity in honeybees (Apis mellifera) $ .

Nowadays, environmental and biological endpoints can be predicted with in silico approaches if sufficient experimental data of good quality are available. Since the experimental evaluation of acute contact toxicity towards honeybees (Apis mellifera) is a complex and expensive assay, the computational models that follow OECD principles for this endpoint prediction represent important alternatives for safety prioritisation of chemicals, especially pesticides. We developed and validated counter-propagation artificial neural network (CPANN) models for in silico evaluation of toxicity of pesticides towards honeybees by using new in-house software. The data set included 254 pesticides with their toxicological experimental values (acute contact toxicity after 48 h of exposure - LD50 [μg/bee]). The 2D structures of compounds were mathematically represented with 56 Dragon molecular descriptors (MDs). The two-category models were developed to separate compounds as toxic or non-toxic for two different thresholds: (i) toxic when LD50 < 1 μg/bee and (ii) toxic when LD50 < 100 μg/bee. The models give reliable predictions in an external validation set and cover a large structural space. They were applied to a structurally diverse data set of 395 experimentally untested pesticides; 19% of them were predicted as highly toxic towards bees.