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

作者：Binyan Jin, Jialin Cui, Qi He, Huan Xu, Xinpeng Sun, Ziyao Chai, Li Zhang · 发表于：Advanced Agrochem · 年份：2025 · DOI：10.1016/j.aac.2025.10.005 · 被引用次数：5 · 研究领域：Fungal Plant Pathogen Control、Computational Drug Discovery Methods、vaccines and immunoinformatics approaches

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 ...