Towards Rational Pesticide Design with Graph Machine Learning Models for Ecotoxicology
This work addresses the need for safer, eco-friendly pesticides for agriculture, but it is incremental as it focuses on dataset creation and preliminary evaluations without presenting new methods or significant performance gains.
The research tackled the problem of rational pesticide design by creating ApisTox, the largest curated dataset on pesticide toxicity to honey bees, and evaluating machine learning models for molecular graph classification, finding that methods from medicinal chemistry often fail to generalize to agrochemicals.
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.