Liu Zhe, Lin Yufan, He Qi, Dai Lingjie, Tan Qinyan, Jin Binyan, Lee Philip W, Zhang Xiaoming, Zhang Li
Innovation Center of Pesticide Research, Department of Applied Chemistry, College of Science, China Agricultural University, Beijing 100193, China.
Key Laboratory of National Forestry and Grassland Administration on Pest Chemical Control, China Agricultural University, Beijing 100193, China.
Molecules. 2025 Jan 9;30(2):234. doi: 10.3390/molecules30020234.
The metabolism of environmental organic chemicals often relies on the catalytic action of specific enzymes at the nanoscale, which is critical for assessing their environmental impact, safety, and efficacy. Hydrolysis is one of the primary metabolic and degradation reaction pathways. Traditionally, hydrolysis product identification has relied on experimental methods that are both time-consuming and costly. In this study, machine-learning-based atomic-driven models were constructed to predict the hydrolysis reactions for environmental organic chemicals, including four main hydrolysis sites: N-Hydrolysis, O-Hydrolysis, C-Hydrolysis, and Global-Hydrolysis. These machine learning models were further integrated with a knowledge-based expert system to create a global hydrolysis model, which utilizes predicted hydrolysis site probabilities to prioritize potential hydrolysis products. For an external test set of 75 chemicals, the global hydrolysis site prediction model achieved an accuracy of 93%. Additionally, among 99 experimental hydrolysis products, our model successfully predicted 90, with a hit rate of 90%. This model offers significant potential for identifying hydrolysis metabolites in environmental organic chemicals.
环境有机化学物质的代谢通常依赖于纳米尺度下特定酶的催化作用，这对于评估它们的环境影响、安全性和功效至关重要。水解是主要的代谢和降解反应途径之一。传统上，水解产物的鉴定依赖于既耗时又昂贵的实验方法。在本研究中，构建了基于机器学习的原子驱动模型来预测环境有机化学物质的水解反应，包括四个主要水解位点：N-水解、O-水解、C-水解和整体水解。这些机器学习模型进一步与基于知识的专家系统集成，以创建一个全局水解模型，该模型利用预测的水解位点概率对潜在的水解产物进行优先级排序。对于75种化学物质的外部测试集，全局水解位点预测模型的准确率达到了93%。此外，在99种实验水解产物中，我们的模型成功预测了90种，命中率为90%。该模型在识别环境有机化学物质中的水解代谢物方面具有巨大潜力。
