Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application.

Jiacheng Xiong; Rongrong Cui; Zhaojun LI; Wei Zhang; Runze Zhang; Zunyun FU; Xiaohong Liu; Zhenghao LI; Kaixian Chen; Mingyue Zheng

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Transfer learning enhanced graph neural network for aldehyde oxidase metabolism prediction and its experimental application.

Author: Jiacheng XIONG ¹ ; Rongrong CUI ¹ ; Zhaojun LI ² ; Wei ZHANG ¹ ; Runze ZHANG ¹ ; Zunyun FU ¹ ; Xiaohong LIU ³ ; Zhenghao LI ⁴ ; Kaixian CHEN ¹ ; Mingyue ZHENG ¹
Author Information

1. Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China.
2. College of Computer and Information Engineering, Dezhou University, Dezhou 253023, China.
3. AI Department, Suzhou Alphama Biotechnology Co., Ltd., Suzhou 215000, China.
4. Shanghai Institute for Advanced Immunochemical Studies, and School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China.
Publication Type:Journal Article
Keywords: Aldehyde oxidase; Drug metabolism; Graph neural network; Kinase inhibitor; Transfer learning
From: Acta Pharmaceutica Sinica B 2024;14(2):623-634
CountryChina
Language:English
Abstract: Aldehyde oxidase (AOX) is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other xenobiotics. AOX-mediated metabolism can result in unexpected outcomes, such as the production of toxic metabolites and high metabolic clearance, which can lead to the clinical failure of novel therapeutic agents. Computational models can assist medicinal chemists in rapidly evaluating the AOX metabolic risk of compounds during the early phases of drug discovery and provide valuable clues for manipulating AOX-mediated metabolism liability. In this study, we developed a novel graph neural network called AOMP for predicting AOX-mediated metabolism. AOMP integrated the tasks of metabolic substrate/non-substrate classification and metabolic site prediction, while utilizing transfer learning from ¹³C nuclear magnetic resonance data to enhance its performance on both tasks. AOMP significantly outperformed the benchmark methods in both cross-validation and external testing. Using AOMP, we systematically assessed the AOX-mediated metabolism of common fragments in kinase inhibitors and successfully identified four new scaffolds with AOX metabolism liability, which were validated through in vitro experiments. Furthermore, for the convenience of the community, we established the first online service for AOX metabolism prediction based on AOMP, which is freely available at https://aomp.alphama.com.cn.