Predicting Toxicity toward Nitrifiers by Attention-Enhanced Graph Neural Networks and Transfer Learning from Baseline Toxicity.

ABSTRACT: 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 compar

SUBMITTER: Zhang K 

PROVIDER: S-EPMC11912336 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature