{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T12:05:13Z","timestamp":1780488313927,"version":"3.54.1"},"reference-count":60,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Lingang Laboratory","award":["LG202102-01-02"],"award-info":[{"award-number":["LG202102-01-02"]}]},{"name":"Strategic Priority Research Program of Chinese Academy of Sciences","award":["SIMM040201"],"award-info":[{"award-number":["SIMM040201"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81773634"],"award-info":[{"award-number":["81773634"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Identifying the potential compound\u2013protein interactions (CPIs) plays an essential role in drug development. The computational approaches for CPI prediction can reduce time and costs of experimental methods and have benefited from the continuously improved graph representation learning. However, most of the network-based methods use heterogeneous graphs, which is challenging due to their complex structures and heterogeneous attributes. Therefore, in this work, we transformed the compound\u2013protein heterogeneous graph to a homogeneous graph by integrating the ligand-based protein representations and overall similarity associations. We then proposed an Inductive Graph AggrEgator-based framework, named CPI-IGAE, for CPI prediction. CPI-IGAE learns the low-dimensional representations of compounds and proteins from the homogeneous graph in an end-to-end manner. The results show that CPI-IGAE performs better than some state-of-the-art methods. Further ablation study and visualization of embeddings reveal the advantages of the model architecture and its role in feature extraction, and some of the top ranked CPIs by CPI-IGAE have been validated by a review of recent literature. The data and source codes are available at https:\/\/github.com\/wanxiaozhe\/CPI-IGAE.<\/jats:p>","DOI":"10.1093\/bib\/bbac073","type":"journal-article","created":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T12:27:24Z","timestamp":1644841644000},"source":"Crossref","is-referenced-by-count":23,"title":["An inductive graph neural network model for compound\u2013protein interaction prediction based on a homogeneous graph"],"prefix":"10.1093","volume":"23","author":[{"given":"Xiaozhe","family":"Wan","sequence":"first","affiliation":[{"name":"State Key Laboratory of Drug Research , Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolong","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Drug Research , Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; School of Pharmacy, East China University of Science and Technology, Shanghai 200237 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dingyan","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Drug Research , Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No.19A Yuquan Road, Beijing 100049 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqin","family":"Tan","sequence":"additional","affiliation":[{"name":"ByteDance AI Lab , Shanghai 201103 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohong","family":"Liu","sequence":"additional","affiliation":[{"name":"AlphaMa Inc. , No. 108, Yuxin Road, Suzhou Industrial Park, Suzhou 215128 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zunyun","family":"Fu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Drug Research , Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hualiang","family":"Jiang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Drug Research , Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China; School of Life Science and Technology, ShanghaiTech University, 393 Huaxiazhong Road, Shanghai 200031 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyue","family":"Zheng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Drug Research , Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9547-0643","authenticated-orcid":false,"given":"Xutong","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Drug Research , Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,3,12]]},"reference":[{"key":"2022072511291505200_ref1","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1093\/bib\/bbt056","article-title":"Similarity-based machine learning methods for predicting drug\u2013target interactions: a brief review","volume":"15","author":"Ding","year":"2013","journal-title":"Brief Bioinform"},{"key":"2022072511291505200_ref2","doi-asserted-by":"crossref","first-page":"4406","DOI":"10.1093\/bioinformatics\/btaa524","article-title":"TransformerCPI: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments","volume":"36","author":"Chen","year":"2020","journal-title":"Bioinformatics"},{"key":"2022072511291505200_ref3","doi-asserted-by":"crossref","first-page":"8723","DOI":"10.1021\/acs.jmedchem.9b00855","article-title":"Deep learning enhancing kinome-wide polypharmacology profiling: model construction and experiment validation","volume":"63","author":"Li","year":"2020","journal-title":"J Med Chem"},{"key":"2022072511291505200_ref4","doi-asserted-by":"crossref","first-page":"13384","DOI":"10.3390\/molecules200713384","article-title":"Molecular docking and structure-based drug design strategies","volume":"20","author":"Ferreira","year":"2015","journal-title":"Molecules"},{"key":"2022072511291505200_ref5","doi-asserted-by":"crossref","first-page":"4331","DOI":"10.3390\/ijms20184331","article-title":"Molecular docking: shifting paradigms in drug discovery","volume":"20","author":"Pinzi","year":"2019","journal-title":"Int J Mol Sci"},{"key":"2022072511291505200_ref6","doi-asserted-by":"crossref","first-page":"30","DOI":"10.2174\/1570163815666180219112421","article-title":"Molecular docking in formulation and development","volume":"16","author":"Kaur","year":"2019","journal-title":"Curr Drug Discov Technol"},{"key":"2022072511291505200_ref7","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1016\/j.csbj.2021.03.004","article-title":"A review on compound-protein interaction prediction methods: data, format, representation and model","volume":"19","author":"Lim","year":"2021","journal-title":"Comput Struct Biotechnol J"},{"key":"2022072511291505200_ref8","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1093\/bib\/bbaa044","article-title":"Identifying drug\u2013target interactions based on graph convolutional network and deep neural network","volume":"22","author":"Zhao","year":"2020","journal-title":"Brief Bioinform"},{"key":"2022072511291505200_ref9","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1021\/ci100050t","article-title":"Extended-connectivity fingerprints","volume":"50","author":"Rogers","year":"2010","journal-title":"J Chem Inf Model"},{"key":"2022072511291505200_ref10","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1021\/ci010132r","article-title":"Reoptimization of MDL keys for use in drug discovery","volume":"42","author":"Durant","year":"2002","journal-title":"J Chem Inf Comput Sci"},{"key":"2022072511291505200_ref11","doi-asserted-by":"crossref","first-page":"8700","DOI":"10.1073\/pnas.92.19.8700","article-title":"Prediction of protein folding class using global description of amino acid sequence","volume":"92","author":"Dubchak","year":"1995","journal-title":"Proc Natl Acad Sci U S A"},{"key":"2022072511291505200_ref12","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1006\/jmbi.1999.3091","article-title":"Protein secondary structure prediction based on position-specific scoring matrices1 1Edited by G. 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