A computational model that accurately predicts the toxicity of pesticides to bees would be useful (i) as a toxicity filter in virtual and experimental screenings of compounds for pesticide activity; (ii) in emergency situations where an immediate assessment of toxicity risk is needed; and (iii) to focus scrutiny and motivate more thorough toxicity assessments on existing and new pesticides predicted to be toxic. Generally, training machine learning models to predict the toxicity of compounds to biological organisms is an active area of research . And, indeed, open data from bee toxicity experiments have been leveraged to train machine learning models to computationally predict the toxicity of pesticides to bees .