{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T03:33:05Z","timestamp":1784431985532,"version":"3.55.0"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T00:00:00Z","timestamp":1692316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072206"],"award-info":[{"award-number":["62072206"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62102158"],"award-info":[{"award-number":["62102158"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Accurate prediction of molecular properties is an important topic in drug discovery. Recent works have developed various representation schemes for molecular structures to capture different chemical information in molecules. The atom and motif can be viewed as hierarchical molecular structures that are widely used for learning molecular representations to predict chemical properties. Previous works have attempted to exploit both atom and motif to address the problem of information loss in single representation learning for various tasks. To further fuse such hierarchical information, the correspondence between learned chemical features from different molecular structures should be considered. Herein, we propose a novel framework for molecular property prediction, called hierarchical molecular graph neural networks (HimGNN). HimGNN learns hierarchical topology representations by applying graph neural networks on atom- and motif-based graphs. In order to boost the representational power of the motif feature, we design a Transformer-based local augmentation module to enrich motif features by introducing heterogeneous atom information in motif representation learning. Besides, we focus on the molecular hierarchical relationship and propose a simple yet effective rescaling module, called contextual self-rescaling, that adaptively recalibrates molecular representations by explicitly modelling interdependencies between atom and motif features. Extensive computational experiments demonstrate that HimGNN can achieve promising performances over state-of-the-art baselines on both classification and regression tasks in molecular property prediction.<\/jats:p>","DOI":"10.1093\/bib\/bbad305","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T14:03:09Z","timestamp":1692367389000},"source":"Crossref","is-referenced-by-count":53,"title":["HimGNN: a novel hierarchical molecular graph representation learning framework for property prediction"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6714-5237","authenticated-orcid":false,"given":"Shen","family":"Han","sequence":"first","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9673-6845","authenticated-orcid":false,"given":"Haitao","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyang","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Plant Science and Technology, Huazhong Agricultural University , People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ganglan","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenyu","family":"Song","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , People\u2019s Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongfei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Computer Science Department, Binghamton University , Binghamton, NY , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7217-4462","authenticated-orcid":false,"given":"Shichao","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , People\u2019s Republic of China and Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Animal Farming Technology, Ministry of Agriculture, Huazhong Agricultural University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5221-2628","authenticated-orcid":false,"given":"Wen","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University , People\u2019s Republic of China and Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Animal Farming Technology, Ministry of Agriculture, Huazhong Agricultural University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"issue":"33","key":"2023092216554359700_ref1","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.ddtec.2020.05.001","article-title":"Molecular property prediction: recent trends in the era of artificial intelligence","volume":"32","author":"Shen","year":"2019","journal-title":"Drug Discov Today Technol"},{"key":"2023092216554359700_ref2","first-page":"1263","article-title":"Neural message passing for quantum chemistry","volume-title":"Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research","author":"Gilmer","year":"2017"},{"key":"2023092216554359700_ref3","article-title":"Convolutional networks on graphs for learning molecular fingerprints","volume-title":"Advances in Neural Information Processing Systems","author":"Duvenaud","year":"2015"},{"issue":"8","key":"2023092216554359700_ref4","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1007\/s10822-016-9938-8","article-title":"Molecular graph convolutions: moving beyond fingerprints","volume":"30","author":"Kearnes","year":"2016","journal-title":"J Comput Aided Mol Des"},{"issue":"1","key":"2023092216554359700_ref5","doi-asserted-by":"crossref","first-page":"13890","DOI":"10.1038\/ncomms13890","article-title":"Quantum-chemical insights from deep tensor neural networks","volume":"8","author":"Sch\u00fctt","year":"2017","journal-title":"Nat Commun"},{"issue":"8","key":"2023092216554359700_ref6","doi-asserted-by":"crossref","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","article-title":"Analyzing learned molecular representations for property prediction","volume":"59","author":"Yang","year":"2019","journal-title":"J Chem Inf Model"},{"issue":"7","key":"2023092216554359700_ref7","doi-asserted-by":"crossref","first-page":"2003","DOI":"10.1093\/bioinformatics\/btac039","article-title":"Cross-dependent graph neural networks for molecular property prediction","volume":"38","author":"Ma","year":"2022","journal-title":"Bioinformatics"},{"issue":"5594","key":"2023092216554359700_ref8","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1126\/science.298.5594.824","article-title":"Network motifs: simple building blocks of complex networks","volume":"298","author":"Milo","year":"2002","journal-title":"Science"},{"key":"2023092216554359700_ref9","first-page":"15870","article-title":"Motif-based graph self-supervised learning for molecular property prediction","volume":"34","author":"Zhang","year":"2021","journal-title":"Adv Neural Inf Process Syst"},{"issue":"9","key":"2023092216554359700_ref10","doi-asserted-by":"crossref","first-page":"2579","DOI":"10.1093\/bioinformatics\/btac112","article-title":"Advanced graph and sequence neural networks for molecular property prediction and drug discovery","volume":"38","author":"Wang","year":"2022","journal-title":"Bioinformatics"},{"key":"2023092216554359700_ref11","article-title":"Hierarchical inter-message passing for learning on molecular graphs","volume-title":"ICML Graph Representation Learning and Beyond (GRL+) Workhop","author":"Fey","year":"2020"},{"key":"2023092216554359700_ref12","first-page":"25581","article-title":"Molecular representation learning via heterogeneous motif graph neural networks","volume-title":"International Conference on Machine Learning","author":"Zhaoning","year":"2022"},{"key":"2023092216554359700_ref13","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"2023092216554359700_ref14","article-title":"Graph attention networks","volume-title":"International Conference on Learning Representations","author":"Veli\u010dkovi\u0107","year":"2018"},{"key":"2023092216554359700_ref15","article-title":"Fragment-based multi-view molecular contrastive learning","volume-title":"Workshop on \u201cMachine Learning for Materials\u201d ICLR 2023","author":"Kim","year":"2023"},{"issue":"1","key":"2023092216554359700_ref16","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1021\/acs.jcim.2c01099","article-title":"Hignn: a hierarchical informative graph neural network for molecular property prediction equipped with feature-wise attention","volume":"63","author":"Zhu","year":"2023","journal-title":"J Chem Inf Model"},{"issue":"1","key":"2023092216554359700_ref17","first-page":"4","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Zonghan","year":"2020","journal-title":"IEEE Transactions Neural Netw Learning Syst"},{"key":"2023092216554359700_ref18","first-page":"2831","article-title":"Communicative representation learning on attributed molecular graphs","volume-title":"IJCAI","author":"Song","year":"2020"},{"issue":"6","key":"2023092216554359700_ref19","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac408","article-title":"FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction","volume":"23","author":"Cai","year":"2022","journal-title":"Brief Bioinform"},{"key":"2023092216554359700_ref20","doi-asserted-by":"crossref","first-page":"2531","DOI":"10.1007\/s00018-005-5368-9","article-title":"Lactoferrin: molecular structure, binding properties and dynamics of lactoferrin","volume":"62","author":"Baker","year":"2005","journal-title":"Cell Mol Life Sci"},{"key":"2023092216554359700_ref21","article-title":"Graph-based molecular representation learning","author":"Guo","year":"2022","journal-title":"arXiv preprint arXiv:220704869"},{"issue":"1","key":"2023092216554359700_ref22","doi-asserted-by":"crossref","first-page":"bbac566","DOI":"10.1093\/bib\/bbac566","article-title":"CasANGCL: pre-training and fine-tuning model based on cascaded attention network and graph contrastive learning for molecular property prediction","volume":"24","author":"Zheng","year":"2023","journal-title":"Brief Bioinform"},{"key":"2023092216554359700_ref23","first-page":"1","article-title":"Graph polish: a novel graph generation paradigm for molecular optimization","volume":"34","author":"Ji","year":"2021","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2023092216554359700_ref24","article-title":"Learning multimodal graph-to-graph translation for molecule optimization","volume-title":"International Conference on Learning Representations","author":"Jin","year":"2019"},{"key":"2023092216554359700_ref25","doi-asserted-by":"crossref","first-page":"108581","DOI":"10.1016\/j.patcog.2022.108581","article-title":"Structure-aware conditional variational auto-encoder for constrained molecule optimization","volume":"126","author":"Junchi","year":"2022","journal-title":"Pattern Recognit"},{"key":"2023092216554359700_ref26","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.neunet.2023.03.034","article-title":"Few-shot molecular property prediction via hierarchically structured learning on relation graphs","volume":"163","author":"Wei","year":"2023","journal-title":"Neural Netw"},{"issue":"8","key":"2023092216554359700_ref27","doi-asserted-by":"crossref","first-page":"3770","DOI":"10.1021\/acs.jcim.0c00502","article-title":"Uncertainty quantification using neural networks for molecular property prediction","volume":"60","author":"Hirschfeld","year":"2020","journal-title":"J Chem Inf Model"},{"key":"2023092216554359700_ref28","first-page":"1144","article-title":"GNN-FiLM: graph neural networks with feature-wise linear modulation","volume-title":"Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research","author":"Brockschmidt","year":"2020"},{"key":"2023092216554359700_ref29","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"2023092216554359700_ref30","first-page":"4171","article-title":"Bert: pre-training of deep bidirectional transformers for language understanding","volume-title":"Proceedings of NAACL-HLT","author":"Devlin","year":"2019"},{"key":"2023092216554359700_ref31","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","volume-title":"International Conference on Learning Representations","author":"Dosovitskiy","year":"2021"},{"key":"2023092216554359700_ref32","article-title":"Self-supervised graph transformer on large-scale molecular data","volume":"33","author":"Rong","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"2023092216554359700_ref33","article-title":"Molecule attention transformer","author":"Maziarka","year":"2020","journal-title":"arXiv preprint arXiv:200208264"},{"key":"2023092216554359700_ref34","article-title":"Substructure-atom cross attention for molecular representation learning","author":"Kim","year":"2023"},{"key":"2023092216554359700_ref35","doi-asserted-by":"crossref","first-page":"120005","DOI":"10.1016\/j.eswa.2023.120005","article-title":"Few-shot learning with transformers via graph embeddings for molecular property prediction","volume":"225","author":"Torres","year":"2023","journal-title":"Expert Syst Appl"},{"key":"2023092216554359700_ref36","article-title":"Molecular joint representation learning via multi-modal information of smiles and graphs","author":"Tianyu","year":"2023","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"22","key":"2023092216554359700_ref37","doi-asserted-by":"crossref","first-page":"5361","DOI":"10.1021\/acs.jcim.2c00798","article-title":"Relmole: molecular representation learning based on two-level graph similarities","volume":"62","author":"Ji","year":"2022","journal-title":"J Chem Inf Model"},{"key":"2023092216554359700_ref38","doi-asserted-by":"crossref","DOI":"10.3115\/v1\/D14-1179","article-title":"Learning phrase representations using RNN encoder-decoder for statistical machine translation","author":"Cho","year":"2014","journal-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)"},{"key":"2023092216554359700_ref39","doi-asserted-by":"crossref","first-page":"6303","DOI":"10.1109\/TSP.2020.3033962","article-title":"Gated graph recurrent neural networks","volume":"68","author":"Ruiz","year":"2020","journal-title":"IEEE Trans Signal Process"},{"key":"2023092216554359700_ref40","first-page":"3734","article-title":"Self-attention graph pooling","volume-title":"International Conference on Machine Learning","author":"Lee","year":"2019"},{"issue":"12","key":"2023092216554359700_ref41","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1038\/s42256-021-00418-8","article-title":"Geometric deep learning on molecular representations","volume":"3","author":"Atz","year":"2021","journal-title":"Nat Mach Intell"},{"key":"2023092216554359700_ref42","first-page":"807","article-title":"Rectified linear units improve restricted Boltzmann machines","volume-title":"Proceedings of the 27th International Conference on Machine Learning (ICML-10)","author":"Nair","year":"2010"},{"key":"2023092216554359700_ref43","doi-asserted-by":"crossref","first-page":"3909","DOI":"10.1145\/3534678.3539192","article-title":"Friend recommendations with self-rescaling graph neural networks","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Song","year":"2022"},{"issue":"2","key":"2023092216554359700_ref44","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1039\/C7SC02664A","article-title":"Moleculenet: a benchmark for molecular machine learning","volume":"9","author":"Zhenqin","year":"2018","journal-title":"Chem Sci"},{"key":"2023092216554359700_ref45","doi-asserted-by":"crossref","DOI":"10.1021\/acs.jcim.3c00059","article-title":"Enhancing molecular representations via graph transformation layers","author":"Ren","year":"2023","journal-title":"J Chem Inf Model"},{"issue":"1","key":"2023092216554359700_ref46","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1038\/s42004-023-00825-5","article-title":"Hierarchical molecular graph self-supervised learning for property prediction","volume":"6","author":"Zang","year":"2023","journal-title":"Commun Chem"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/5\/bbad305\/51711324\/bbad305.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/5\/bbad305\/51711324\/bbad305.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T17:17:49Z","timestamp":1695403069000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbad305\/7245716"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,18]]},"references-count":46,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,9,20]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbad305","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,9]]},"published":{"date-parts":[[2023,8,18]]},"article-number":"bbad305"}}