(1) Background: Machine learning algorithms are finding fruitful applications in predicting the ADME profile of new molecules, with a particular focus on metabolism predictions. However, the development of comprehensive metabolism predictors is hampered by the lack of highly accurate metabolic resources. Hence, we recently proposed a manually curated metabolic database (MetaQSAR), the level of accuracy of which is well suited to the development of predictive models. (2) Methods: MetaQSAR was used to extract datasets to predict the metabolic reactions subdivided into major classes, classes and subclasses. The collected datasets comprised a total of 3788 first-generation metabolic reactions. Predictive models were developed by using standard random forest algorithms and sets of physicochemical, stereo-electronic and constitutional descriptors. (3) Results: The developed models showed satisfactory performance, especially for hydrolyses and conjugations, while redox reactions were predicted with greater difficulty, which was reasonable as they depend on many complex features that are not properly encoded by the included descriptors. (4) Conclusions: The generated models allowed a precise comparison of the propensity of each metabolic reaction to be predicted and the factors affecting their predictability were discussed in detail. Overall, the study led to the development of a freely downloadable global predictor, MetaClass, which correctly predicts 80% of the reported reactions, as assessed by an explorative validation analysis on an external dataset, with an overall MCC = 0.44.

中文翻译：

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MetaClass，一种基于 MetaQSAR 数据库预测代谢反应发生的综合分类系统

(1) 背景：机器学习算法在预测新分子的 ADME 谱方面取得了卓有成效的应用，尤其是代谢预测。然而，由于缺乏高度准确的代谢资源，综合代谢预测因子的发展受到阻碍。因此，我们最近提出了一个手动管理的代谢数据库（MetaQSAR），其准确度水平非常适合预测模型的开发。(2)方法：利用MetaQSAR提取数据集预测代谢反应，细分为大类、大类和亚类。收集的数据集包括总共 3788 个第一代代谢反应。预测模型是通过使用标准随机森林算法和物理化学、立体电子和体质描述符集开发的。(3) 结果：开发的模型表现出令人满意的性能，特别是对于水解和结合，而预测氧化还原反应的难度更大，这是合理的，因为它们依赖于许多复杂的特征，这些特征没有被包含的描述符正确编码。（4。结论：生成的模型允许对每个代谢反应的预测倾向进行精确比较，并详细讨论了影响其可预测性的因素。总体而言，该研究导致开发了一个可免费下载的全局预测器 MetaClass，它正确预测了 80% 的报告反应，通过对外部数据集的探索性验证分析进行评估，总体 MCC = 0.44。