Assessing chemical environmental impacts is critical but challenging due to the time-consuming nature of experimental testing. Graph neural networks (GNNs) support superior prediction performance and mechanistic interpretation of (eco-)toxicity data, but face the risk of overfitting on the typically small experimental data sets. In contrast to purely data-driven approaches, we propose a mechanism-guided transfer learning strategy that is highly efficient and provides key insights into the underlying drivers of (eco-)toxicity. By leveraging the mechanistic link between baseline toxicity and toxicity toward nitrifiers, we pretrained a GNN on lipophilicity data (log P) and subsequently fine-tuned it on the limited data set of toxicity toward nitrifiers, achieving prediction performance comparable with pretraining on much larger but mechanistically less relevant data sets. Additionally, we enhanced GNN interpretability by adjusting multihead attentions after convolutional layers to identify key substructures, and quantified their contributions using a Shapley Value method adapted for graph-structured data with improved computational efficiency. The highlighted substructures aligned well with and effectively distinguished known structural alerts for baseline toxicity and specific modes of toxic action in nitrifiers. The proposed strategy will allow uncovering new structural alerts in other (eco)toxicity data, and thus foster new mechanistic insights to support chemical risk assessment and safe-by-design principles.