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Updated: May 9, 2026

Protocols for Testing the Toxicity of Novel Insecticidal Chemistries to Mosquitoes

Protocols for Testing the Toxicity of Novel Insecticidal Chemistries to Mosquitoes

Published on: February 13, 2019

APPi: A Multiscale Qualitative-Quantitative Insecticide-Likeness Evaluation Platform and Application.

Jia-Lin Cui¹, Qi He¹, Bin-Yan Jin¹

¹Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing, China.

Plant Biotechnology Journal

|July 22, 2025

Summary

This summary is machine-generated.

Researchers developed the Agrochem Predictive Platform for Insecticide-likeness (APPi) to improve insecticide discovery. This platform enhances virtual screening and lead compound optimization, achieving 85% accuracy in predicting insecticide effectiveness.

Keywords:

fragment score insecticide‐likeness machine learning multiscale evaluation web visualiser

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Topical Application Bioassay to Quantify Insecticide Toxicity for Mosquitoes and Fruit Flies

Topical Application Bioassay to Quantify Insecticide Toxicity for Mosquitoes and Fruit Flies

Published on: January 19, 2022

Area of Science:

Agricultural Chemistry
Computational Chemistry
Toxicology

Background:

Pests cause significant global crop losses annually, necessitating effective pest control strategies.
Increasing insecticide resistance and limitations of traditional pesticide evaluation methods highlight the need for novel approaches.
The 'insecticide-likeness' concept focuses on in vivo biological effects for more accurate lead compound evaluation.

Purpose of the Study:

To develop a comprehensive platform for evaluating insecticide-likeness, integrating qualitative and quantitative methods.
To establish precise rules and models for predicting the biological activity of potential insecticides.
To aid in the early stages of insecticide discovery through improved virtual screening and structure optimization.

Main Methods:

Development of the Agrochem Predictive Platform for Insecticide-likeness (APPi).
Proposal of qualitative evaluation rules based on physicochemical properties (e.g., ClogP, HBA, HBD).
Creation of a quantitative model (APPi model) using a multi-classifier machine learning framework (PUMV).
Development of the FragScore Visualiser tool to identify key insecticidal fragments.

Main Results:

The APPi platform integrates qualitative rules and a quantitative machine learning model.
The APPi model achieved 85% accuracy on an independent external test set, outperforming existing models.
The FragScore Visualiser aids in identifying critical insecticidal fragments within compounds.

Conclusions:

The APPi platform offers a robust tool for the precise evaluation of insecticide lead compounds.
This approach significantly improves the accuracy of predicting insecticide effectiveness in early discovery phases.
The APPi platform is freely accessible, supporting advancements in insecticide research and development.