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

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Acta Pharmaceutica Sinica B 2024年第2期14卷 623-634页

作者： Jiacheng Xiong Rongrong Cui Zhaojun Li Wei Zhang Runze Zhang zunyun fu Xiaohong Liu Zhenghao Li Kaixian Chen Mingyue Zheng Drug Discovery and Design Center State Key Laboratory of Drug ResearchShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai 201203China University of Chinese Academy of Sciences Beijing 100049China College of Computer and Information Engineering Dezhou UniversityDezhou 253023China AI Department Suzhou Alphama Biotechnology Co.Ltd.Suzhou 215000China Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and TechnologyShanghaiTech UniversityShanghai 200031China State Key Laboratory of Pharmaceutical Biotechnology Nanjing UniversityNanjing 210023China

Aldehyde oxidase(AOX)is a molybdoenzyme that is primarily expressed in the liver and is involved in the metabolism of drugs and other ***-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 *** 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 *** this study,we developed a novel graph neural network called AOMP for predicting AOX-mediated *** 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 *** significantly outperformed the benchmark methods in both cross-validation and external *** 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 ***,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://***.

关键词： Drugmetabolism Aldehyde oxidase Transferlearning Graph neural network Kinase inhibitor

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αExtractor: a system for automatic extraction of chemical information from biomedical literature

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Science China(Life Sciences) 2024年第3期67卷 618-621页

作者： Jiacheng Xiong Xiaohong Liu Zhaojun Li Hongzhong Xiao Guangchao Wang Zhenjiang Niu Chaoyuan Fei Feisheng Zhong Gang Wang Wei Zhang zunyun fu Zhiguo Liu Kaixian Chen Hualiang Jiang Mingyue Zheng Drug Discovery and Design Center State Key Laboratory of Drug ResearchShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai 201203China University of Chinese Academy of Sciences Beijing 100049China AI Department Suzhou Alphama Biotechnology Co.Ltd.Suzhou 215125China College of Computer and Information Engineering Dezhou UniversityDezhou 253023China

Dear Editor,Great progress has been made using artificial intelligence(AI) techniques in learning knowledge from biomedical databases in recent years, revolutionizing the study of many fields, such as protein structure prediction and protein design(Madani et al., 2023). However, there is massive biomedical knowledge not curated in the form of structured data but hidden in primary scientific literature.

关键词： has such knowledge

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Drug target inference by mining transcriptional data using a novel graph convolutional network framework

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Protein & Cell 2022年第4期13卷 281-301页

作者： Feisheng Zhong Xiaolong Wu Ruirui Yang Xutong Li Dingyan Wang zunyun fu Xiaohong Liu XiaoZhe Wan Tianbiao Yang Zisheng Fan Yinghui Zhang Xiaomin Luo Kaixian Chen Sulin Zhang Hualiang Jiang Mingyue Zheng Drug Discovery and Design Center State Key Laboratory of Drug ResearchShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai 201203China University of Chinese Academy of Sciences Beijing 100049China School of Pharmacy East China University of Science and TechnologyShanghai 200237China Nanjing University of Chinese Medicine Nanjing 210023China Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and TechnologyShanghaiTech UniversityShanghai 200031China

A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or offtarget ***,the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA ***,we propose a novel Siamese spectral-based graph convolutional network(SSGCN)model for inferring the protein targets of chemical compounds from gene transcriptional *** the gene signature of a compound perturbation only provides indirect clues of the interacting targets,and the biological networks under different experiment conditions further complicate the situation,the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown *** a benchmark set and a large time-split validation dataset,the model achieved higher target inference accuracy as compared to previous methods such as Connectivity *** experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound,or reversely,in finding novel inhibitors of a given target of interest.

关键词： drug target inference transcriptomics deep learning experimental verification

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Artificial intelligence in drug design

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Science China(Life Sciences) 2018年第10期61卷 1191-1204页

作者： Feisheng Zhong Jing Xing Xutong Li Xiaohong Liu zunyun fu Zhaoping Xiong Dong Lu Xiaolong Wu Jihui Zhao Xiaoqin Tan Fei Li Xiaomin Luo Zhaojun Li Kaixian Chen Mingyue Zheng Hualiang Jiang Drug Discovery and Design Center State Key Laboratory of Drug Research Shanghai Institute of Materia Medica Chinese Academy of SciencesShanghai 201203China School of Pharmacy University of Chinese Academy of SciencesBeijing 100049 China School of Life Science and Technology ShanghaiTech UniversityShanghai 200031 China Department of Chemistry College of Sciences Shanghai UniversityShanghai 200444 China School of Information Management Dezhou UniversityDezhou 253023 China

Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology,the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical *** to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence(AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening,activity scoring, quantitative structure-activity relationship(QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity(ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability,deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules,which will further promote the application of AI technologies in the field of drug design.

关键词：人工智能设计机器学习理论计算化学归纳能力电脑辅助物理解释特征抽取

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Cu催化偶联反应合成烷基芳基醚的DFT机理研究

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化学学报 2021年第7期79卷 948-952页

作者：满清敏付尊蕴刘甜甜郑明月蒋华良中国科学技术大学化学物理系合肥230026 中国科学院上海药物研究所药物发现与设计中心上海201203

本工作提出了Cu催化的2-(2-碘苯基)-1-乙醇分子内UllmannC―O偶联反应机理,并用密度泛函理论(density functionaltheory,DTF)计算验证.催化循环从原位生成的亚铜配合物开始,亚铜配合物经氧化加成生成Cu(Ⅲ)配合物;Cu(Ⅲ)配合物和反应物... 详细信息

本工作提出了Cu催化的2-(2-碘苯基)-1-乙醇分子内UllmannC―O偶联反应机理,并用密度泛函理论(density functionaltheory,DTF)计算验证.催化循环从原位生成的亚铜配合物开始,亚铜配合物经氧化加成生成Cu(Ⅲ)配合物;Cu(Ⅲ)配合物和反应物醇配位形成四配位Cu(Ⅲ)配合物;然后四配位Cu(Ⅲ)配合物被O-卤键活化,活化后进行配体交换;最后经还原消除释放烷基芳基醚并再生催化剂.量化计算显示叔丁醇离子不仅是一种碱而且还扮演催化剂物种的角色.然后,计算各步反应能垒表明配体交换是整个反应的决速步骤.计算结果与实验结果吻合较好,证明了计算机理的合理性.

关键词：密度泛函理论铜催化 Ullmann C―O偶联叔丁醇离子

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秦岭丹江上游流域水源涵养功能时空变化特征及其影响因素

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煤田地质与勘探 2023年第5期51卷 133-145页

作者：杨晶羽舒遵赟杨甫付德亮马丽张丽维薛宇泽西北大学城市与环境学院陕西西安710127 中国科学院水利部水土保持研究所陕西杨凌712100 陕西省煤田地质集团有限公司自然资源部煤炭资源勘查与综合利用重点实验室陕西西安710026

良好的水源涵养功能可以调节区域水文循环和保障区域生态安全,与人类生产生活密切相关,如何精准评估水源涵养功能已成为当下生态水文学的研究热点。基于分布式水文土壤植被模型(DHSVM)和水量平衡法,分析秦岭丹江上游流域2010—2020年水... 详细信息

良好的水源涵养功能可以调节区域水文循环和保障区域生态安全,与人类生产生活密切相关,如何精准评估水源涵养功能已成为当下生态水文学的研究热点。基于分布式水文土壤植被模型(DHSVM)和水量平衡法,分析秦岭丹江上游流域2010—2020年水源涵养量的时空变化规律,并探明气候变化和土地利用变化对流域水源涵养功能的影响。结果表明:(1)丹江上游流域年水源涵养量平均值为61.60 mm,月水源涵养量平均值为4.3 mm,有年际和年内变化大的特征。(2)水文要素中,降水量、蒸散发量和径流量11年都呈增长趋势,水源涵养量为减少趋势。流域内水源涵养量的空间分布规律为由东南部向西北方向递减。(3)不同时间尺度下影响水源涵养量的主要气象因子不同。在年际尺度下,影响水源涵养量的主要气象驱动因子为降水,表现为促进作用;年内尺度下影响水源涵养量的主要气象因子为降水和气温,分别表现为促进作用和抑制作用。(4)丹江上游流域主要土地利用类型水源涵养量由高到低依次为:草地、林地、耕地。在土地利用类型变化后,草地面积增加,林地和耕地面积减少,流域水源涵养功能提高。本研究可为当地水资源管理和制定生态保护决策提供科学依据。

关键词：水源涵养 DHSVM 水量平衡法丹江流域气象因子

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