Pesti-DGI-Net: A multi-modal deep learning architecture based on dual interpretability for pesticide-likeness prediction

Abstract

“Pesticide-likeness” represents an extension of the pharmaceutical concept of “drug-likeness” to the field of pesticides. The development of algorithmic tools for predicting pesticide-likeness holds great significance for the rational design of pesticide molecules. Among various computational approaches, artificial intelligence techniques, especially deep learning, stand out due to their distinctive advantages. However, the application of deep learning models in the field of pesticide-likeness remains relatively limited. To address this gap, we proposed a multi-modal deep learning architecture, termed Pesti-DGI-Net, which took the standard Simplified Molecular Input Line Entry System (SMILES) of compounds as input and combined molecular representations across multiple dimensions. Through this fusion, Pesti-DGI-Net made accurate predictions of the pesticide-likeness for candidate compounds, as substantiated by extensive evaluations on internal test sets and an external independent test set. Additionally, Pesti-DGI-Net provided two interpretable methods to elucidate the relationship between chemical structure and pesticide-likeness. Comparison with domain experts showed that Pesti-DGI-Net enabled researchers to better understand the prediction results. Finally, we integrated Pesti-DGI-Net with existing web resources to comprehensively assess the potential of compounds as pesticide-like molecules. Our cloud platform is freely available at http://chemyang.ccnu.edu.cn/ccb/server/CoPLE/.