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

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外文摘要：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 un-expected 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 chem-ists 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 devel-oped a novel graph neural network called AOMP for predicting AOX-mediated metabolism.AOMP in-tegrated 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 avail-able at https://aomp.alphama.com.cn.

外文关键词：

Drug metabolismAldehyde oxidaseTransfer learningGraph neural networkKinase inhibitor

作者：

Jiacheng Xiong、Rongrong Cui、Zhaojun Li、Wei Zhang、Runze Zhang、Zunyun Fu、Xiaohong Liu、Zhenghao Li、Kaixian Chen、Mingyue Zheng

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作者单位：

Drug Discovery and Design Center,State Key Laboratory of Drug Research,Shanghai Institute of Materia Medica,Chinese Academy of Sciences,Shanghai 201203,China

University of Chinese Academy of Sciences,Beijing 100049,China

College of Computer and Information Engineering,Dezhou University,Dezhou 253023,China

AI Department,Suzhou Alphama Biotechnology Co.,Ltd.,Suzhou 215000,China

Shanghai Institute for Advanced Immunochemical Studies,and School of Life Science and Technology,ShanghaiTech University,Shanghai 200031,China

State Key Laboratory of Pharmaceutical Biotechnology,Nanjing University,Nanjing 210023,China

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基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaLingang LaboratoryNational Key Research and Development Program of Chinaopen fund of state key laboratory of Pharmaceutical Biotechnology,Nanjing University,China

项目编号：

T222500282273855LG202102-01-022022YFC3400504KF-202301

出版年：

2024

DOI：

10.1016/j.apsb.2023.10.008

药学学报(英文版)

CSTPCD

ISSN：

年,卷(期)：2024.14(2)

参考文献量44