RÉSUMÉ
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.