BeeEcoTox: A Large Language Model-Empowered Graph-Learning Framework for Predicting Ecotoxicity of Agrochemicals to Bees

Agrochemical exposure can threaten bees with substantial ecological risks. The rapid and accurate prediction of agrochemical ecotoxicity to bees is thus urgently needed; however, existing models are constrained by single-type structural inputs, resulting in limited accuracy and generalizability. This study presents BeeEcoTox, a multimodal graph-learning framework for predicting agrochemical ecotoxicity to bees. The model fuses ChemFM-derived semantic features with molecular graphs through a graph isomorphism network with internal batch normalization, combined with structural features from 1,139 agrochemicals. Inherent class imbalance in the curated data set is addressed through a cost-sensitive learning approach to ensure that the model prioritizes high recall in detecting ecotoxic agrochemicals without compromising overall performance. BeeEcoTox achieves state-of-the-art performance, with area under the curve and recall values of 0.91 and 0.90, respectively, following rigorous benchmarking against a suite of baseline models. Model explainability is enhanced through the use of GNNExplainer, a model-agnostic approach for identifying key toxicophores. BeeEcoTox is deployed as a publicly available web-based platform (https://www.ai4environ.cn/BeeEcoTox) to support advanced ecological risk assessments of agrochemicals and the development of new approach methodologies.