UDP-glucuronosyltransferases (UGTs) have gained increasing attention as they play important roles in the phase II metabolism of drugs. Due to the time-consuming process and high cost of experimental approaches to identify the metabolic fate of UGT enzymes, in silico methods have been developed to predict the UGT-mediated metabolism of drug-like molecules. We developed consensus models with the combination of machine learning (ML) and graph neural network (GNN) methods to predict if a drug-like molecule is a potential UGT substrate, and then we applied the Weisfeiler-Lehman Network (WLN) model to identify the sites of metabolism (SOMs) of UGT-catalyzed substrates. For the substrate model, the accuracy of the single substrate prediction model on the test set could reach to 0.835. Compared with the single estimators, the consensus models are more stable and have better generalization ability, and the accuracy on the test set reached to 0.851. For the SOM model, the top-1 accuracy of the SOM model on the test set reached to 0.898, outperforming existing works. Thus, in this study, we proposed a computational framework, named Meta-UGT, which would provide a useful tool for the prediction and optimization of metabolic profiles and drug design.

中文翻译：

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通过图神经网络在计算机上预测 UGT 介导的药物样分子代谢

UDP-葡萄糖醛酸转移酶（UGTs）因其在药物II期代谢中发挥重要作用而受到越来越多的关注。由于鉴定 UGT 酶代谢命运的实验方法耗时且成本高，因此已开发出计算机方法来预测 UGT 介导的药物样分子代谢。我们结合机器学习 (ML) 和图神经网络 (GNN) 方法开发了共识模型来预测药物样分子是否是潜在的 UGT 底物，然后我们应用 Weisfeiler-Lehman 网络 (WLN) 模型来识别UGT催化底物的代谢位点（SOM）。对于基板模型，单基板预测模型在测试集上的准确率可以达到0.835。与单一估计量相比，共识模型更稳定，泛化能力更强，在测试集上的准确率达到0.851。对于 SOM 模型，SOM 模型在测试集上的 top-1 准确率达到了 0.898，优于现有工作。因此，在本研究中，我们提出了一个名为 Meta-UGT 的计算框架，它将为预测和优化代谢谱和药物设计提供有用的工具。