{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T20:04:39Z","timestamp":1784750679883,"version":"3.55.0"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62061160369"],"award-info":[{"award-number":["62061160369"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,19]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Understanding the mechanisms of candidate\u00a0drugs play an important role in drug discovery. The activating\/inhibiting mechanisms between drugs and targets are major types of mechanisms of drugs. Owing to the complexity of drug\u2013target (DT) mechanisms and data scarcity, modelling this problem based on deep learning methods to accurately predict DT activating\/inhibiting mechanisms remains a considerable challenge. Here, by considering network pharmacology, we propose a multi-view deep learning model, DrugAI, which combines four modules, i.e. a graph neural network for drugs, a convolutional neural network for targets, a network embedding module for drugs and targets and a deep neural network for predicting activating\/inhibiting mechanisms between drugs and targets. Computational experiments show that DrugAI performs better than state-of-the-art methods and has good robustness and generalization. To demonstrate the reliability of the predictive results of DrugAI, bioassay experiments are conducted to validate two drugs (notopterol and alpha-asarone) predicted to activate TRPV1. Moreover, external validation bears out 61 pairs of mechanism relationships between natural products and their targets predicted by DrugAI based on independent literatures and PubChem bioassays. DrugAI, for the first time, provides a powerful multi-view deep learning framework for robust prediction of DT activating\/inhibiting mechanisms.<\/jats:p>","DOI":"10.1093\/bib\/bbac526","type":"journal-article","created":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T15:01:17Z","timestamp":1671289277000},"source":"Crossref","is-referenced-by-count":38,"title":["DrugAI: a multi-view deep learning model for predicting drug\u2013target activating\/inhibiting mechanisms"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8355-2465","authenticated-orcid":false,"given":"Siqin","family":"Zhang","sequence":"first","affiliation":[{"name":"Tsinghua University Institute for TCM-X, MOE Key Laboratory of Bioinformatics\/Bioinformatics Division, BNRIST, Department of Automation, , Beijing 100084 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuo","family":"Yang","sequence":"additional","affiliation":[{"name":"Tsinghua University Institute for TCM-X, MOE Key Laboratory of Bioinformatics\/Bioinformatics Division, BNRIST, Department of Automation, , Beijing 100084 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenhong","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing University of Chinese Medicine Institute for Brain Disorders, Dongzhimen Hospital, , Beijing 100700 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinxing","family":"Lai","sequence":"additional","affiliation":[{"name":"Beijing University of Chinese Medicine Institute for Brain Disorders, Dongzhimen Hospital, , Beijing 100700 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Yang","sequence":"additional","affiliation":[{"name":"Beijing University of Chinese Medicine School of Traditional Chinese Medicine, , Beijing 100029 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianyang","family":"Zeng","sequence":"additional","affiliation":[{"name":"Tsinghua University Institute for Interdisciplinary Information Sciences, , Beijing 100084 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shao","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University Institute for TCM-X, MOE Key Laboratory of Bioinformatics\/Bioinformatics Division, BNRIST, Department of Automation, , Beijing 100084 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"issue":"4","key":"2023011917143976100_ref1","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1038\/nchembio.1199","article-title":"Target identification and mechanism of action in chemical biology and drug discovery","volume":"9","author":"Schenone","year":"2013","journal-title":"Nat Chem Biol"},{"issue":"D1","key":"2023011917143976100_ref2","doi-asserted-by":"crossref","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","article-title":"DrugBank 5.0: a major update to the DrugBank database for 2018","volume":"46","author":"Wishart","year":"2018","journal-title":"Nucl Acids Res"},{"issue":"1","key":"2023011917143976100_ref3","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/s41467-017-00680-8","article-title":"A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information","volume":"8","author":"Luo","year":"2017","journal-title":"Nat Commun"},{"issue":"1","key":"2023011917143976100_ref4","doi-asserted-by":"crossref","first-page":"5221","DOI":"10.1038\/s41467-019-12928-6","article-title":"A Bayesian machine learning approach for drug target identification using diverse data types","volume":"10","author":"Madhukar","year":"2019","journal-title":"Nat Commun"},{"key":"2023011917143976100_ref5","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1038\/s41467-017-00680-8","article-title":"A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information","volume":"8","author":"Luo","year":"2017","journal-title":"Nat Commun"},{"issue":"4","key":"2023011917143976100_ref6","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.cels.2020.03.002","article-title":"MONN: a multi-objective neural network for predicting compound-protein interactions and affinities","volume":"10","author":"Li","year":"2020","journal-title":"Cell Syst"},{"issue":"23","key":"2023011917143976100_ref7","doi-asserted-by":"crossref","first-page":"4485","DOI":"10.1093\/bioinformatics\/btab473","article-title":"MultiDTI: drug-target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical entities and known heterogeneous network","volume":"37","author":"Zhou","year":"2021","journal-title":"Bioinformatics"},{"issue":"2","key":"2023011917143976100_ref8","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1038\/s42256-020-0152-y","article-title":"Predicting drug-protein interaction using quasi-visual question answering system","volume":"2","author":"Zheng","year":"2020","journal-title":"Nat Mach Intel"},{"issue":"16","key":"2023011917143976100_ref9","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"},{"issue":"9","key":"2023011917143976100_ref10","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1021\/acs.jcim.9b00387","article-title":"Predicting drug-target interaction using a novel graph neural network with 3D structure-embedded graph representation","volume":"59","author":"Lim","year":"2019","journal-title":"J Chem Inf Model"},{"issue":"10","key":"2023011917143976100_ref11","doi-asserted-by":"crossref","first-page":"4131","DOI":"10.1021\/acs.jcim.9b00628","article-title":"Graph convolutional neural networks for predicting drug-target interactions","volume":"59","author":"Torng","year":"2019","journal-title":"J Chem Inf Model"},{"key":"2023011917143976100_ref12","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.csbj.2019.02.002","article-title":"Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network","volume":"17","year":"2019","journal-title":"Comput Struct Biotechnol J"},{"issue":"Supp 1","key":"2023011917143976100_ref13","first-page":"17","article-title":"Heterogeneous network propagation for herb target identification","volume":"18","year":"2018","journal-title":"BMC Medical Inform Decis"},{"issue":"5","key":"2023011917143976100_ref14","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1016\/j.drudis.2022.02.023","article-title":"Compound-protein interaction prediction by deep learning: databases, descriptors and models","volume":"27","author":"Du","year":"2022","journal-title":"Drug Discov Today"},{"issue":"13","key":"2023011917143976100_ref15","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1093\/bioinformatics\/btt234","article-title":"Predicting drug-target interactions using restricted Boltzmann machines","volume":"29","author":"Wang","year":"2013","journal-title":"Bioinformatics"},{"issue":"5460","key":"2023011917143976100_ref16","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1126\/science.287.5460.1969","article-title":"Mechanism-based target identification and drug discovery in cancer research","volume":"287","author":"Gibbs","year":"2000","journal-title":"Science"},{"key":"2023011917143976100_ref17","doi-asserted-by":"crossref","DOI":"10.1101\/2021.03.18.436088","article-title":"Predicting activatory and inhibitory drug-target interactions based on mol2vec and genetically perturbed transcriptomes","author":"Lee","year":"2021"},{"issue":"1","key":"2023011917143976100_ref18","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1038\/s41598-017-18315-9","article-title":"Predicting inhibitory and activatory drug targets by chemically and genetically perturbed transcriptome signatures","volume":"8","author":"Sawada","year":"2018","journal-title":"Sci Rep"},{"issue":"3","key":"2023011917143976100_ref19","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1038\/s42256-020-00285-9","article-title":"A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing","volume":"3","author":"Pham","year":"2021","journal-title":"Nat Mach Intel"},{"issue":"7553","key":"2023011917143976100_ref20","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"8","key":"2023011917143976100_ref21","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"},{"key":"2023011917143976100_ref22","volume-title":"29th Annual Conference on Neural Information Processing Systems (NIPS)","author":"Duvenaudt","year":"2015"},{"issue":"1","key":"2023011917143976100_ref23","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1186\/s12859-017-1702-0","article-title":"3D deep convolutional neural networks for amino acid environment similarity analysis","volume":"18","author":"Torng","year":"2017","journal-title":"BMC Bioinform"},{"issue":"6","key":"2023011917143976100_ref24","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun ACM"},{"key":"2023011917143976100_ref25","volume-title":"A Tutorial on Network Embeddings","author":"Haochen Chen","year":"2018"},{"issue":"D1","key":"2023011917143976100_ref26","doi-asserted-by":"crossref","first-page":"D605","DOI":"10.1093\/nar\/gkaa1074","article-title":"The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene\/measurement sets","volume":"49","author":"Szklarczyk","year":"2021","journal-title":"Nucl Acids Res"},{"issue":"D1","key":"2023011917143976100_ref27","doi-asserted-by":"crossref","first-page":"D930","DOI":"10.1093\/nar\/gky1075","article-title":"ChEMBL: towards direct deposition of bioassay data","volume":"47","author":"Mendez","year":"2019","journal-title":"Nucl Acids Res"},{"key":"2023011917143976100_ref28","doi-asserted-by":"crossref","first-page":"W623","DOI":"10.1093\/nar\/gkp456","article-title":"PubChem: a public information system for analyzing bioactivities of small molecules","volume":"37","author":"Wang","year":"2009","journal-title":"Nucl Acids Res"},{"issue":"D1","key":"2023011917143976100_ref29","doi-asserted-by":"crossref","first-page":"D480","DOI":"10.1093\/nar\/gkaa1100","article-title":"UniProt: the universal protein knowledgebase in 2021","volume":"49","author":"Bateman","year":"2021","journal-title":"Nucl Acids Res"},{"key":"2023011917143976100_ref30","doi-asserted-by":"crossref","DOI":"10.1007\/978-981-16-0753-0","volume-title":"Network Pharmacology","author":"Li","year":"2021"},{"issue":"6","key":"2023011917143976100_ref31","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"},{"issue":"11","key":"2023011917143976100_ref32","doi-asserted-by":"crossref","first-page":"4337","DOI":"10.1073\/pnas.0607879104","article-title":"Predicting protein-protein interactions based only on sequences information","volume":"104","author":"Shen","year":"2007","journal-title":"Proc Natl Acad Sci USA"},{"key":"2023011917143976100_ref33","doi-asserted-by":"crossref","first-page":"701","DOI":"10.1145\/2623330.2623732","volume-title":"Proceedings of the 20th Acm Sigkdd International Conference on Knowledge Discovery and Data Mining (Kdd'14)","author":"Perozzi","year":"2014"},{"issue":"2","key":"2023011917143976100_ref34","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Mining Knowl Discovery"},{"issue":"1","key":"2023011917143976100_ref35","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"issue":"1","key":"2023011917143976100_ref36","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans Inform Theory"},{"issue":"16","key":"2023011917143976100_ref37","doi-asserted-by":"crossref","first-page":"8749","DOI":"10.1021\/acs.jmedchem.9b00959","article-title":"Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism","volume":"63","author":"Xiong","year":"2020","journal-title":"J Med Chem"},{"key":"2023011917143976100_ref38"},{"key":"2023011917143976100_ref39"},{"key":"2023011917143976100_ref40","article-title":"Neural message passing for quantum chemistry","volume":"70","author":"Gilmer","year":"2017","journal-title":"Int Conf Mach Learn"},{"key":"2023011917143976100_ref41","volume-title":"22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)","author":"Grover","year":"2016"},{"key":"2023011917143976100_ref42","volume-title":"24th International Conference on World Wide Web (WWW)","author":"Tang","year":"2015"},{"key":"2023011917143976100_ref43","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1145\/2939672.2939753","volume-title":"Kdd'16: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining","author":"Wang","year":"2016"},{"key":"2023011917143976100_ref44","doi-asserted-by":"crossref","first-page":"6656","DOI":"10.1038\/ncomms7656","article-title":"Honokiol blocks and reverses cardiac hypertrophy in mice by activating mitochondrial Sirt3","volume":"6","author":"Pillai","year":"2015","journal-title":"Nat Commun"},{"issue":"26","key":"2023011917143976100_ref45","doi-asserted-by":"crossref","first-page":"E5896","DOI":"10.1073\/pnas.1801745115","article-title":"Chemoproteomics reveals baicalin activates hepatic CPT1 to ameliorate diet-induced obesity and hepatic steatosis","volume":"115","author":"Dai","year":"2018","journal-title":"Proc Natl Acad Sci USA"},{"issue":"29","key":"2023011917143976100_ref46","doi-asserted-by":"crossref","first-page":"E5986","DOI":"10.1073\/pnas.1706778114","article-title":"Highly selective inhibition of IMPDH2 provides the basis of antineuroinflammation therapy","volume":"114","author":"Liao","year":"2017","journal-title":"Proc Natl Acad Sci USA"},{"issue":"2","key":"2023011917143976100_ref47","doi-asserted-by":"crossref","DOI":"10.1002\/advs.202270009","article-title":"Discovery of herbacetin as a novel SGK1 inhibitor to alleviate myocardial hypertrophy","volume":"9","author":"Zhang","year":"2022","journal-title":"Adv Sci"},{"issue":"11","key":"2023011917143976100_ref48","doi-asserted-by":"crossref","DOI":"10.1016\/j.celrep.2020.108158","article-title":"The natural compound notopterol binds and targets JAK2\/3 to ameliorate inflammation and arthritis","volume":"32","author":"Wang","year":"2020","journal-title":"Cell Rep"},{"issue":"8","key":"2023011917143976100_ref49","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1161\/CIRCRESAHA.119.315861","article-title":"Celastrol attenuates angiotensin II-induced cardiac remodeling by targeting STAT3","volume":"126","author":"Ye","year":"2020","journal-title":"Circ Res"},{"issue":"1674\u20137267","key":"2023011917143976100_ref50","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1360\/SSI-2021-0243","article-title":"Principle, method and application of relationship inference based on biological networks","volume":"52","author":"Li","year":"2022","journal-title":"Sci Sinica Inform"},{"issue":"1","key":"2023011917143976100_ref51","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"Zhuang","year":"2021","journal-title":"Proc IEEE"},{"key":"2023011917143976100_ref52","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/B978-0-12-373717-5.00018-X","volume-title":"The Practitioner's Guide to Data Quality Improvement","author":"Loshin","year":"2011"},{"issue":"4","key":"2023011917143976100_ref53","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1021\/ci9803381","article-title":"Unsupervised data base clustering based on daylight's fingerprint and tanimoto similarity: a fast and automated way to cluster small and large data sets","volume":"39","author":"Butina","year":"1999","journal-title":"J Chem Inf Comput Sci"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/1\/bbac526\/48783370\/bbac526.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/1\/bbac526\/48783370\/bbac526.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T18:00:53Z","timestamp":1728324053000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac526\/6918762"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,16]]},"references-count":53,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1,19]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac526","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,1]]},"published":{"date-parts":[[2022,12,16]]},"article-number":"bbac526"}}