Journal of Zhejiang University SCIENCE  B

Accepted manuscript available online (unedited version)


A novel graph-text composite model for pesticide metabolic pathway prediction


Author(s):  Ruyue LI1, 2, Fahui YUAN1, Mengcen WANG3, Yong HE1, Yufei LIU1

Affiliation(s):  1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China; more

Corresponding email(s):  yufeiliu@zju.edu.cn

Key Words:  Pesticide metabolism, Deep learning, Multimodal fusion, Metabolic pathway prediction


Ruyue LI1,2, Fahui YUAN1, Mengcen WANG3, Yong HE1, Yufei LIU1. A novel graph-text composite model for pesticide metabolic pathway prediction[J]. Journal of Zhejiang University Science B,in press.Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/jzus.B2600409

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author="Ruyue LI1,2, Fahui YUAN1, Mengcen WANG3, Yong HE1, Yufei LIU1",
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publisher="Zhejiang University Press & Springer",
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Abstract: 
Agricultural chemicals are essential in modern agriculture for managing pests and diseases and increasing crop yields (Barone et al., 2024). However, the pervasive use of pesticides has raised primary concerns over potential risks to human health, as well as serious questions about environmental contamination and ecological hazards (Collinge et al., 2022). Phytotransformation of pesticides can influence their efficacy, the extent of plant uptake, and potential environmental exposure (Li et al., 2019). Therefore, elucidating the key metabolic pathways within the crop interleaf system is crucial for the design of safer and greener pesticides.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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On-line Access: 2026-10-22

Received: 2026-06-20

Revision Accepted: 2026-10-13

Crosschecked: 0000-00-00

Cited: 0

Clicked: 19

Citations:  Bibtex RefMan EndNote GB/T7714

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