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Updated: Jun 9, 2025

Topical Application Bioassay to Quantify Insecticide Toxicity for Mosquitoes and Fruit Flies
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Improved Ensemble Model for Insecticide Recognition by Incorporating Insect Toxicity Data.

Ruoqi Yang1,2, Zhepeng Ma2, Zhiheng Wei2

  • 1National Pesticide Engineering Research Center, State Key Laboratory of Elemento-Organic Chemistry, College of Chemistry, Nankai University, Tianjin 300071, P.R. China.

Journal of Agricultural and Food Chemistry
|October 23, 2024
PubMed
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Artificial intelligence (AI) models for insecticide discovery were improved by incorporating insect toxicity data, enhancing performance and generalization. An online platform was developed for free access to insecticide screening, successfully identifying two novel pesticide molecules.

Area of Science:

  • Agricultural Chemistry
  • Computational Chemistry
  • Toxicology

Background:

  • Traditional pesticide development is inefficient and costly.
  • Limited commercial pesticide data hinders artificial intelligence (AI) model performance and generalization.
  • Integrating domain knowledge is crucial for improving AI in pesticide discovery.

Purpose of the Study:

  • To enhance AI model performance for insecticide recognition by incorporating insect toxicity data.
  • To develop an ensemble model for superior predictive accuracy.
  • To create a publicly accessible online platform for insecticide screening.

Main Methods:

  • Incorporation of insect toxicity domain knowledge into AI models.
  • Development and validation of AI models using enhanced datasets.
Keywords:
Plutella xylostellaartificial intelligenceinsecticidetoxicity datavirtual screening

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  • Ensemble modeling by integrating diverse individual models.
  • Creation of an online platform for insecticide screening (InsectiVS).

    • Main Results:

    • AI models trained with added insect toxicity data demonstrated improved external validation performance and generalization.
    • Ensemble models achieved superior performance compared to individual models.
    • Two novel insecticide molecules with activity against *Plutella xylostella* were identified.
    • An online platform (InsectiVS) was launched for public access to screening tools.

    Conclusions:

    • Integrating domain knowledge, specifically insect toxicity data, significantly improves AI-driven insecticide discovery.
    • Ensemble modeling offers enhanced performance in pesticide research.
    • The developed online platform facilitates broader access to AI tools for agricultural chemistry research.
    • This approach bridges AI and agricultural chemistry, promising wide applications in pesticide development.