Improved Ensemble Model for Insecticide Recognition by Incorporating Insect Toxicity Data.

Abstract

Pesticide molecules, such as insecticides, play a critical role in modern agricultural production. Traditional pesticide development methods are often inefficient and expensive, while data-driven artificial intelligence (AI) techniques have emerged as a useful tool to facilitate drug discovery. However, currently available commercial pesticide data is limited, which makes the trained models unsatisfactory in terms of performance and generalization. From a domain knowledge perspective, insect toxicity data were incorporated to improve the insecticide recognition of AI models. Compared to the models trained with the original data set, the new models performed better in the external validation, and their generalization was more desirable. In addition, by integrating different types of individual models, we obtained an ensemble model with better performance. Based on this, an online platform was developed to provide researchers with free access to insecticide screening (https://dpai.ccnu.edu.cn/InsectiVS/). Finally, two potential insecticide molecules with insecticidal activity against Plutella xylostella were successfully identified in a real-world scenario. In conclusion, this idea connects the fields of AI and agricultural chemistry and is expected to have wide application in pesticide research.