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. We evaluate ApisTox using a diverse suite of machine learning approaches, 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 toward the agrochemical domain.

本研究解决了当前农药毒性数据稀缺的问题，特别是蜜蜂的种特异性毒性。通过使用多种机器学习方法评估ApisTox数据集，发现现有模型在非医学数据集（如ApisTox）上的表现退化，突显了针对农业化学品领域模型开发的必要性。研究的主要发现是现有的最佳算法在生物医学数据上训练，无法很好地推广到其他领域。

评估机器学习模型预测农药对蜜蜂的毒性