Huang Mengting, Lou Chaofeng, Wu Zengrui, Li Weihua, Lee Philip W, Tang Yun, Liu Guixia
Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
J Cheminform. 2022 Jul 8;14(1):46. doi: 10.1186/s13321-022-00626-3.
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.
尿苷二磷酸葡萄糖醛酸转移酶（UGTs）因其在药物Ⅱ相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模拟方法来预测UGT介导的类药物分子代谢。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，以预测类药物分子是否为潜在的UGT底物，然后应用魏斯费勒-莱曼网络（WLN）模型来识别UGT催化底物的代谢位点（SOMs）。对于底物模型，测试集上单一底物预测模型的准确率可达0.835。与单一估计器相比，共识模型更稳定，具有更好的泛化能力，测试集上的准确率达到0.851。对于SOM模型，测试集上SOM模型的top-1准确率达到0.898，优于现有研究。因此，在本研究中，我们提出了一个名为Meta-UGT的计算框架，它将为代谢谱预测和优化以及药物设计提供一个有用的工具。
