{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T22:53:45Z","timestamp":1783205625040,"version":"3.54.6"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T00:00:00Z","timestamp":1658188800000},"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\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFA1000102\uff0c2021YFA1000103"],"award-info":[{"award-number":["2021YFA1000102\uff0c2021YFA1000103"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873280"],"award-info":[{"award-number":["61873280"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972416"],"award-info":[{"award-number":["61972416"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Taishan Scholarship","award":["tsqn201812029"],"award-info":[{"award-number":["tsqn201812029"]}]},{"name":"Foundation of Science and Technology Development of Jinan","award":["201907116"],"award-info":[{"award-number":["201907116"]}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["ZR2021QF023"],"award-info":[{"award-number":["ZR2021QF023"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["21CX06018A"],"award-info":[{"award-number":["21CX06018A"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Spanish Project","award":["PID2019-106960GB-I00"],"award-info":[{"award-number":["PID2019-106960GB-I00"]}]},{"name":"Juan de la Cierva","award":["IJC2018-038539-I"],"award-info":[{"award-number":["IJC2018-038539-I"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,9,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Multi-drug combinations for the treatment of complex diseases are gradually becoming an important treatment, and this type of treatment can take advantage of the synergistic effects among drugs. However, drug\u2013drug interactions (DDIs) are not just all beneficial. Accurate and rapid identifications of the DDIs are essential to enhance the effectiveness of combination therapy and avoid unintended side effects. Traditional DDIs prediction methods use only drug sequence information or drug graph information, which ignores information about the position of atoms and edges in the spatial structure. In this paper, we propose Molormer, a method based on a lightweight attention mechanism for DDIs prediction. Molormer takes the two-dimension (2D) structures of drugs as input and encodes the molecular graph with spatial information. Besides, Molormer uses lightweight-based attention mechanism and self-attention distilling to process spatially the encoded molecular graph, which not only retains the multi-headed attention mechanism but also reduces the computational and storage costs. Finally, we use the Siamese network architecture to serve as the architecture of Molormer, which can make full use of the limited data to train the model for better performance and also limit the differences to some extent between networks dealing with drug features. Experiments show that our proposed method outperforms state-of-the-art methods in Accuracy, Precision, Recall and F1 on multi-label DDIs dataset. In the case study section, we used Molormer to make predictions of new interactions for the drugs Aliskiren, Selexipag and Vorapaxar and validated parts of the predictions. Code and models are available at https:\/\/github.com\/IsXudongZhang\/Molormer.<\/jats:p>","DOI":"10.1093\/bib\/bbac296","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T20:38:51Z","timestamp":1658176731000},"source":"Crossref","is-referenced-by-count":57,"title":["Molormer: a lightweight self-attention-based method focused on spatial structure of molecular graph for drug\u2013drug interactions prediction"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2671-8275","authenticated-orcid":false,"given":"Xudong","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum , Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum , Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangyu","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum , Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum , Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5281-6189","authenticated-orcid":false,"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum , Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alfonso","family":"Rodriguez-Paton","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Faculty of Computer Science, Polytechnical University of Madrid, Campus de Montegancedo , Boadilla del Monte 28660, Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8910-0929","authenticated-orcid":false,"given":"Jianmin","family":"Wang","sequence":"additional","affiliation":[{"name":"The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicin, Yonsei University , Incheon 21983, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2741-433X","authenticated-orcid":false,"given":"Xun","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum , Qingdao 266580, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,7,19]]},"reference":[{"issue":"5","key":"2022092013211933400_ref1","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1038\/nbt.3834","article-title":"Synergistic drug combinations for cancer identified in a crispr screen for pairwise genetic interactions","volume":"35","author":"Han","year":"2017","journal-title":"Nat Biotechnol"},{"issue":"6","key":"2022092013211933400_ref2","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1049\/iet-syb.2008.0166","article-title":"Detecting drug targets with minimum side effects in metabolic networks","volume":"3","author":"Li","year":"2009","journal-title":"IET Syst Biol"},{"key":"2022092013211933400_ref3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.artmed.2018.03.001","article-title":"Position-aware deep multi-task learning for drug\u2013drug interaction extraction","volume":"87","author":"Zhou","year":"2018","journal-title":"Artificial intel ligence in medicine"},{"key":"2022092013211933400_ref4","doi-asserted-by":"crossref","first-page":"6146901","DOI":"10.1155\/2016\/6918381","article-title":"Drug-drug interaction extraction via convolutional neural networks","volume":"2016","author":"Liu","year":"2016","journal-title":"Comput Math Methods Med"},{"issue":"6","key":"2022092013211933400_ref5","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1038\/s42256-020-0189-y","article-title":"A novel machine learning framework for automated biomedical relation extraction from large-scale literature repositories","volume":"2","author":"Hong","year":"2020","journal-title":"Nat Mach Intell"},{"issue":"1","key":"2022092013211933400_ref6","first-page":"1","article-title":"Predicting drug\u2013drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge","volume":"9","author":"Takeda","year":"2017","journal-title":"J Chem"},{"issue":"1","key":"2022092013211933400_ref7","first-page":"1","article-title":"Amde: a novel attention-mechanism-based multidimensional feature encoder for drug\u2013drug interaction prediction","author":"Pang","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022092013211933400_ref8","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymeth.2022.02.007","article-title":"DeepFusion: a deep learning based multi-scale feature fusion method for predicting drug-target interactions","author":"Song","year":"2022","journal-title":"Methods"},{"issue":"8","key":"2022092013211933400_ref9","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.3390\/biom11081119","article-title":"MCN-CPI: multiscale convolutional network for compound-protein interaction prediction","volume":"11","author":"Wang","year":"2021","journal-title":"Biomolecules"},{"issue":"18","key":"2022092013211933400_ref10","doi-asserted-by":"crossref","first-page":"E4304","DOI":"10.1073\/pnas.1803294115","article-title":"Deep learning improves prediction of drug\u2013drug and drug\u2013food interactions","volume":"115","author":"Ryu","year":"2018","journal-title":"Proc Natl Acad Sci"},{"issue":"22\u201323","key":"2022092013211933400_ref11","first-page":"5545","article-title":"Deeppurpose: a deep learning library for drug\u2013target interaction prediction","volume":"36","author":"Huang","year":"2020","journal-title":"Bioinformatics"},{"key":"2022092013211933400_ref12","volume-title":"Advances in Neural Information Processing Systems","author":"Alex","year":"2012"},{"key":"2022092013211933400_ref13","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"},{"key":"2022092013211933400_ref14","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/978-3-642-24797-2_4","volume-title":"Supervised Sequence Labelling with Recurrent Neural Networks","author":"Graves","year":"2012"},{"key":"2022092013211933400_ref15","first-page":"1263","volume-title":"International Conference on Machine Learning, PMLR","author":"Justin","year":"2017"},{"key":"2022092013211933400_ref16","volume-title":"Advances in Neural Information Processing Systems 30","author":"Vaswani","year":"2017"},{"key":"2022092013211933400_ref17","first-page":"974","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Rex","year":"2018"},{"key":"2022092013211933400_ref18","first-page":"7370","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","author":"Yao","year":"2019"},{"key":"2022092013211933400_ref19","first-page":"950","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","author":"Xiang","year":"2019"},{"key":"2022092013211933400_ref20","article-title":"Graph attention networks","volume-title":"Stat","author":"Petar"},{"issue":"5","key":"2022092013211933400_ref21","doi-asserted-by":"crossref","first-page":"102277","DOI":"10.1016\/j.ipm.2020.102277","article-title":"Mgat: multimodal graph attention network for recommendation","volume":"57","author":"Tao","year":"2020","journal-title":"Inf Process Manag"},{"key":"2022092013211933400_ref22","article-title":"Gated graph sequence neural networks","author":"Li","year":"2015","journal-title":"arXiv preprint arXiv:151105493"},{"key":"2022092013211933400_ref23","article-title":"Graph- to-sequence learning using gated graph neural networks","author":"Beck","year":"2018","journal-title":"arXiv preprint arXiv:180609835"},{"issue":"3","key":"2022092013211933400_ref24","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1093\/bib\/bbz042","article-title":"Graph convolutional networks for computational drug development and discovery","volume":"21","author":"Sun","year":"2020","journal-title":"Brief Bioinform"},{"key":"2022092013211933400_ref25","article-title":"Deepdrug: a general graph-based deep learning framework for drug relation prediction","author":"Cao","year":"2020","journal-title":"biorxiv"},{"issue":"1","key":"2022092013211933400_ref26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-019-3013-0","article-title":"Novel deep learning model for more accurate prediction of drug- drug interaction effects","volume":"20","author":"Lee","year":"2019","journal-title":"BMC Bioinformatics"},{"issue":"9","key":"2022092013211933400_ref27","first-page":"1421","article-title":"A novel attention-mechanism based cox survival model by exploiting pan-cancer empirical genomic information","volume":"11","author":"Meng","year":"2022","journal-title":"Cel ls"},{"issue":"5","key":"2022092013211933400_ref28","doi-asserted-by":"crossref","first-page":"644","DOI":"10.3390\/biom12050644","article-title":"Multi-transdti: transformer for drug\u2013target interaction prediction based on simple universal dictionaries with multi-view strategy","volume":"12","author":"Wang","year":"2022","journal-title":"Biomolecules"},{"issue":"1","key":"2022092013211933400_ref29","doi-asserted-by":"crossref","first-page":"bbab421","DOI":"10.1093\/bib\/bbab421","article-title":"MDF-SA-DDI: predicting drug--drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism","volume":"23","author":"Shenggeng","year":"2022","journal-title":"Brief Bioinform"},{"key":"2022092013211933400_ref30","volume-title":"Advances in Neural Information Processing Systems","author":"Ying","year":"2021"},{"issue":"5","key":"2022092013211933400_ref31","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1109\/LGRS.2018.2799232","article-title":"Identifying corresponding patches in SAR and optical images with a pseudo-Siamese CNN","volume":"15","author":"Hughes","year":"2018","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"D1","key":"2022092013211933400_ref32","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":"Nucleic Acids Res"},{"key":"2022092013211933400_ref33","article-title":"Rdkit: a software suite for cheminformatics, computational chemistry, and predictive modeling","volume-title":"Greg Landrum","author":"Landrum","year":"2013"},{"key":"2022092013211933400_ref34","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume-title":"Mach Learn Res","author":"Raffel"},{"key":"2022092013211933400_ref35","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/N18-2074","article-title":"Self- attention with relative position representations","author":"Shaw","year":"2018"},{"key":"2022092013211933400_ref36","volume-title":"Proceedings of AAAI","author":"Haoyi","year":"2021"},{"key":"2022092013211933400_ref37","article-title":"Fast and accurate deep network learning by exponential linear units (elus)","author":"Clevert","year":"2015"},{"issue":"6","key":"2022092013211933400_ref38","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1093\/bioinformatics\/btaa880","article-title":"Moltrans: molecular interaction transformer for drug\u2013 target interaction prediction","volume":"37","author":"Huang","year":"2021","journal-title":"Bioinformatics"},{"issue":"suppl 1","key":"2022092013211933400_ref39","doi-asserted-by":"crossref","first-page":"D901","DOI":"10.1093\/nar\/gkm958","article-title":"Drugbank: a knowledgebase for drugs, drug actions and drug targets","volume":"36","author":"Wishart","year":"2008","journal-title":"Nucleic Acids Res"},{"issue":"7","key":"2022092013211933400_ref40","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A review of recurrent neural networks: Lstm cells and network architectures","volume":"31","author":"Yong","year":"2019","journal-title":"Neural Comput"},{"issue":"3","key":"2022092013211933400_ref41","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1093\/bioinformatics\/btab715","article-title":"Hyperattentiondti: improving drug\u2013protein interaction prediction by sequence-based deep learning with attention mechanism","volume":"38","author":"Zhao","year":"2022","journal-title":"Bioinformatics"},{"key":"2022092013211933400_ref42","volume-title":"Advances in Neural Information Processing Systems","author":"Bromley","year":"1993"},{"issue":"16","key":"2022092013211933400_ref43","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1517\/14656566.8.16.2839","article-title":"Aliskiren, the first in a new class of direct renin inhibitors for hypertension: present and future perspectives","volume":"8","author":"Byung-Hee","year":"2007","journal-title":"Expert Opin Pharmacother"},{"issue":"5","key":"2022092013211933400_ref44","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1177\/1060028015571410","article-title":"Vincent Colucci, Patricia a Howard, Jean M Nappi, and Sarah a Vorapaxar in atherosclerotic disease management","volume":"49","author":"Cheng","year":"2015","journal-title":"Ann Pharmacother"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/5\/bbac296\/45937020\/bbac296.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/5\/bbac296\/45937020\/bbac296.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,20]],"date-time":"2022-09-20T17:40:34Z","timestamp":1663695634000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac296\/6645994"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,19]]},"references-count":44,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,9,20]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac296","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,9]]},"published":{"date-parts":[[2022,7,19]]},"article-number":"bbac296"}}