BeeToxAI: An artificial intelligence-based web app to assess acute toxicity of chemicals to honey bees

Abstract

Chemically induced toxicity is the leading cause of recent extinction of honey bees. In this regard, we developed an innovative artificial intelligence-based web app (BeeToxAI) for assessing the acute toxicity of chemicals to Apis mellifera. Initially, we developed and externally validated QSAR models for classification (external set accuracy ∼91%) through the combination of Random Forest and molecular fingerprints to predict the potential for chemicals to cause acute contact toxicity and acute oral toxicity to honey bees. Then, we developed and externally validated regression QSAR models (R2 = 0.75) using Feedforward Neural Networks (FNNs). Afterward, the best models were implemented in the publicly available BeeToxAI web app (http://beetoxai.labmol.com.br/). The outputs of BeeToxAI are: toxicity predictions with estimated confidence, applicability domain estimation, and color-coded maps of relative structure fragment contributions to toxicity. As an additional assessment of BeeToxAI performance, we collected an external set of pesticides with known bee toxicity that were not included in our modeling dataset. BeeToxAI classification models were able to predict four out of five pesticides correctly. The acute contact toxicity model correctly predicted all of the eight pesticides. Here we demonstrate that BeeToxAI can be used as a rapid new approach methodology for predicting acute toxicity of chemicals in honey bees.

Keywords

• Apis mellifera
• Artificial intelligence
• Pollinators
• Ecotoxicology
• Machine learning
• Predictive modeling