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Fragment-based deep molecular generation using hierarchical chemical graph representation and multi-resolution graph variational autoencoder.

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第一作者： Zhenxiang,Gao

第一单位： Department of Computer Science and Engineering, University of Connecticut, Storrs, 06269, CT.;Current address: Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, 44106, OH.

作者： Zhenxiang,Gao ^[1] ; Xinyu,Wang ^[1] ; Blake,Blumenfeld Gaines ^[1] ; Xuetao,Shi ^[2] ; Jinbo,Bi ^[2] ; Minghu,Song ^[3]

作者单位： Department of Computer Science and Engineering, University of Connecticut, Storrs, 06269, CT.;Current address: Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, 44106, OH. ^[1] Department of Computer Science and Engineering, University of Connecticut, Storrs, 06269, CT.;Current address: Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, 44106, OH.;Department of Biomedical Engineering, University of Connecticut, Storrs, 06269, CT. ^[2] Department of Biomedical Engineering, University of Connecticut, Storrs, 06269, CT. ^[3]

关键词 deep molecular generation fragment-based molecular generation graph-based deep generative model variational autoencoder

医学主题词药物发现(Drug Discovery);模型, 分子(Models, Molecular)

DOI 10.1002/minf.202200215

PMID 36764926

发布时间 2025-01-06

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