Advancing ecotoxicity assessment: Leveraging pre-trained model for bee toxicity and compound degradability prediction

Xinkang Li, Feng Zhang, Liangzhen Zheng, Jingjing Guo

Research output: Contribution to journalArticlepeer-review

Abstract

The prediction of ecological toxicity plays an increasingly important role in modern society. However, the existing models often suffer from poor performance and limited predictive capabilities. In this study, we propose a novel approach for ecological toxicity assessment based on pre-trained models. By leveraging pre-training techniques and graph neural network models, we establish a highperformance predictive model. Furthermore, we incorporate a variational autoencoder to optimize the model, enabling simultaneous discrimination of toxicity to bees and molecular degradability. Additionally, despite the low similarity between the endogenous hormones in bees and the compounds in our dataset, our model confidently predicts that these hormones are non-toxic to bees, which further strengthens the credibility and accuracy of our model. We also discovered the negative correlation between the degradation and bee toxicity of compounds. In summary, this study presents an ecological toxicity assessment model with outstanding performance. The proposed model accurately predicts the toxicity of chemicals to bees and their degradability capabilities, offering valuable technical support to relevant fields.

Original languageEnglish
Article number134828
JournalJournal of Hazardous Materials
Volume475
DOIs
Publication statusPublished - 15 Aug 2024

Keywords

  • Bee poisoning
  • Ecological toxicity
  • Molecular degradability
  • Pre-trained model, Graph neural networks

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