用图机器学习加速安全农药设计，解决生态毒性预测难题。
Towards Rational Pesticide Design with Graph Machine Learning Models for Ecotoxicology
- 构建蜂蜜蜂毒性的大型数据集ApisTox，支持农药生态毒性评估。
- 发现药物研发常用模型在农药场景表现不佳，需专用模型。
- 适合从事绿色农药研发与生态毒理学研究的学者参考。
本研究聚焦于基于图机器学习的理性农药设计，旨在加速开发更安全、更环保的农用化学品，借鉴药物发现中的计算机模拟方法。重点面向生态毒理学领域，首次构建了目前最大的蜜蜂毒性注释数据集ApisTox。对多种分子图分类模型（包括分子指纹、图核、图神经网络及预训练变换器）进行了全面评估。结果表明，药物化学中表现优异的模型在农用化学品上泛化能力差，凸显了开发领域专用模型与基准测试体系的必要性。未来工作将建立完整基准套件，并针对农药研发的独特挑战设计专用机器学习模型。
原文摘要 · Abstract (English)
This research focuses on rational pesticide design, using graph machine learning to accelerate the development of safer, eco-friendly agrochemicals, inspired by in silico methods in drug discovery. With an emphasis on ecotoxicology, the initial contributions include the creation of ApisTox, the largest curated dataset on pesticide toxicity to honey bees. We conducted a broad evaluation of machine learning (ML) models for molecular graph classification, including molecular fingerprints, graph kernels, GNNs, and pretrained transformers. The results show that methods successful in medicinal chemistry often fail to generalize to agrochemicals, underscoring the need for domain-specific models and benchmarks. Future work will focus on developing a comprehensive benchmarking suite and designing ML models tailored to the unique challenges of pesticide discovery.
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