research-article

Integrating classic AI and agriculture: : A novel model for predicting insecticide-likeness to enhance efficiency in insecticide development

Authors: Jia-Lin Cui, Hua Li, Qi He, Bin-Yan Jin, Zhe Liu, Xiao-Ming Zhang, Li ZhangAuthors Info & Claims

Volume 112, Issue C

https://doi.org/10.1016/j.compbiolchem.2024.108113

Published: 21 November 2024 Publication History

Abstract

The integration of artificial intelligence (AI) into smart agriculture boosts production and management efficiency, facilitating sustainable agricultural development. In intensive agricultural management, adopting eco-friendly and effective pesticides is crucial to promote green agricultural practices. However, exploring new insecticides species is a difficult and time-consuming task that involves significant risks. Enhancing compound druggability in the lead discovery phase could considerably shorten the discovery cycle, accelerating insecticides research and development. The Insecticide Activity Prediction (IAPred) model, a novel classic artificial intelligence-based method for evaluating the potential insecticidal activity of unknown functional compounds, is introduced in this study. The IAPred model utilized 27 insecticide-likeness features from PaDEL descriptors and employed an ensemble of Support Vector Machine (SVM) and Random Forest (RF) algorithms using the hard-vote mechanism, achieving an accuracy rate of 86 %. Notably, the IAPred model outperforms current models by accurately predicting the efficacy of novel insecticides such as nicofluprole, overcoming the limitations inherent in existing insecticide structures. Our research presents a practical approach for discovering and optimizing novel insecticide lead compounds quickly and efficiently.

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Highlights

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A novel insectide-likeness prediction model (IAPred) was built using the ensemble machine learning strategy.

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The 27 important insecticide-likeness features for IAPred were identified through multi-scale screening.

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The IAPred model could improve the prediction accuracy to 86.7 % and extend the coverage of chemical structure predictions.

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Integrating classic AI and agriculture: A novel model for predicting insecticide-likeness to enhance efficiency in insecticide development

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cover image Computational Biology and Chemistry

Computational Biology and Chemistry Volume 112, Issue C

Oct 2024

820 pages

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Elsevier Ltd.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 21 November 2024

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