The rising levels of chemical pollutants in aquatic ecosystems threaten biodiversity and demand improved methods for assessing ecological risk. Recent deep learning methods advance molecular toxicity prediction but still suffer from limited generalisation, interpretability and robustness under data scarcity. To address these issues, we propose ADFC-ATP, a framework that integrates dual-view molecular graph fusion with contrastive topology learning based on NT-Xent loss. Our approach uses structural graph augmentations during pretraining to enhance robustness, while a graph attention encoder learns hierarchical substructure patterns through masked feature reconstruction. For downstream aquatic toxicity prediction, an adaptive attention-based fusion mechanism dynamically combines pretrained graph embeddings and fingerprint similarity metrics, enabling more accurate and robust toxicity assessment. Experimental results show that on four fish toxicity datasets, the AUC of ADFC-ATP achieves an average relative improvement of approximately 10.2% compared to two classic graph neural network baseline models: single-task graph convolutional network (GCN-ST) and multi-task graph convolutional network (GCN-MT). Ablation and attention weight visualisation confirm the critical roles of scaffold preservation and contrastive regularisation, and highlight our model's ability to identify toxicoph ores consistent with QSAR principles. ADFC-ATP thus provides a robust, interpretable, and computationally efficient tool for predicting toxicity of emerging aquatic contaminants, offering a valuable complement to traditional laboratory testing. ADFC-ATP is freely available at https://github.com/zhaoqi106/ADFC-ATP.
Jia et al. (Sun,) studied this question.