Dual-strategy approach for fungicide discovery: Machine learning-based activity prediction and fragment co-occurrence network construction

The development of fungicides is time-consuming and costly. Introducing a fungicide-likeness assessment strategy at the early screening stage can help reduce development risks and improve the success rate. However, existing assessment methods are often plagued by low accuracy and poor generalization, while fragment-based design strategies commonly fail to account for synergistic effects between structural units. Therefore, based on a small-scale sample set, this study developed a more efficient global predictive model for fungicidal activity—named APPf—by integrating multi-scale feature screening methods and machine learning algorithms, which also accounts for synergistic effects among different structural fragments. We utilized three independent external test sets for model validation: External Test Set 1 for general validation, External Test Set 2 for comparison with existing models, and External Test Set 3 for disease-specific fungicide evaluation. On External Test Set 1, the APPf model achieved a precision of 0.6454, a recall of 0.8535, and an F1 score of 0.7350, demonstrating its robust predictive performance. It also exhibited strong enrichment capability for positive samples in External Test Set 2. For External Test Set 3, APPf achieved a prediction accuracy exceeding 80% for each disease, suggesting its promising potential in practical fungicide development. Furthermore, we quantified the contribution of molecular descriptors to the model predictions using SHAP value analysis and identified nHdNH and NssssNp as strong indicative features for predicting fungicidal activity, thereby enhancing the interpretability of the model. APPf has been deployed on a public web server ( http://pesticides.cau.edu.cn/APPf ), providing a user-friendly online prediction service to support the discovery of novel fungicides. Meanwhile, we employed a molecular fragmentation strategy to analyze the co-occurrence relationships between fragments in fungicides and constructed a network map of fragment co-occurrence associated with fungicidal activity. This study provides both an active fragment library and a global fungicide-likeness assessment tool for AI-based de novo molecular generation aimed at discovering novel fungicidal leads, which is expected to enhance the efficiency of developing new fungicides. • A novel dual-strategy framework that integrates a machine learning-based prediction model (APPf) with a fragment co-occurrence network is proposed for fungicide discovery. • The developed APPf model demonstrates superior performance, achieving >80% accuracy across 12 plant diseases and significantly outperforming existing models in enriching active compounds. • Practical utility is ensured by providing an interactive web server for APPf and a fragment library, offering valuable tools for AI-driven molecular generation.