Evaluating machine learning models for predicting pesticide toxicity to honey bees

Small molecules play a critical role in the biomedical, environmental, and agrochemical domains, each with distinct physicochemical requirements and success criteria. Although biomedical research benefits from extensive datasets and established benchmarks, agrochemical data remain scarce, particularly with respect to species-specific toxicity. This work focuses on ApisTox, the most comprehensive dataset of experimentally validated chemical toxicity to the honey bee ( Apis mellifera ), an ecologically vital pollinator. The primary goal of this study was to determine the suitability of diverse machine learning approaches for modeling such toxicity, including molecular fingerprints, graph kernels, and graph neural networks, as well as pretrained models. Comparative analysis with medicinal datasets from the MoleculeNet benchmark reveals that ApisTox represents a distinct chemical space. Performance degradation on non-medicinal datasets, such as ApisTox, demonstrates their limited generalizability of current state-of-the-art algorithms trained solely on biomedical data. Our study highlights the need for more diverse datasets and for targeted model development geared towards the agrochemical domain. • Benchmarking ML models for pesticide toxicity prediction in honey bees (Apis mellifera). • ApisTox defines a distinct agrochemical chemical space versus MoleculeNet datasets. • Molecular fingerprints and WL-OA graph kernels outperform GNNs and pretrained models. • Pretrained biomedical models show limited transferability to agrochemical toxicity. • Explainable ML reveals chemically meaningful drivers of bee toxicity predictions.