{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T03:43:29Z","timestamp":1784259809236,"version":"3.55.0"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:00:00Z","timestamp":1688083200000},"content-version":"vor","delay-in-days":29,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2023R1A2C3004176"],"award-info":[{"award-number":["NRF-2023R1A2C3004176"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Health & Welfare, Republic of Korea","award":["HR20C0021(3)"],"award-info":[{"award-number":["HR20C0021(3)"]}]},{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Institute for Information & communications Technology Planning & Evaluation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Protein\u2013ligand binding affinity prediction is a central task in drug design and development. Cross-modal attention mechanism has recently become a core component of many deep learning models due to its potential to improve model explainability. Non-covalent interactions (NCIs), one of the most critical domain knowledge in binding affinity prediction task, should be incorporated into protein\u2013ligand attention mechanism for more explainable deep drug\u2013target interaction models. We propose ArkDTA, a novel deep neural architecture for explainable binding affinity prediction guided by NCIs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Experimental results show that ArkDTA achieves predictive performance comparable to current state-of-the-art models while significantly improving model explainability. Qualitative investigation into our novel attention mechanism reveals that ArkDTA can identify potential regions for NCIs between candidate drug compounds and target proteins, as well as guiding internal operations of the model in a more interpretable and domain-aware manner.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability<\/jats:title>\n                  <jats:p>ArkDTA is available at https:\/\/github.com\/dmis-lab\/ArkDTA<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Contact<\/jats:title>\n                  <jats:p>kangj@korea.ac.kr<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad207","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T08:18:20Z","timestamp":1688113100000},"page":"i448-i457","source":"Crossref","is-referenced-by-count":25,"title":["ArkDTA: attention regularization guided by non-covalent interactions for explainable drug\u2013target binding affinity prediction"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6458-7723","authenticated-orcid":false,"given":"Mogan","family":"Gim","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Korea University , Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9548-7146","authenticated-orcid":false,"given":"Junseok","family":"Choe","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Korea University , Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3231-478X","authenticated-orcid":false,"given":"Seungheun","family":"Baek","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Korea University , Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0613-120X","authenticated-orcid":false,"given":"Jueon","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Korea University , Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3778-6177","authenticated-orcid":false,"given":"Chaeeun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Korea University , Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4048-4089","authenticated-orcid":false,"given":"Minjae","family":"Ju","sequence":"additional","affiliation":[{"name":"LG CNS, AI Research Center , Seoul 07795, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sumin","family":"Lee","sequence":"additional","affiliation":[{"name":"LG AI Research , Seoul 07795, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6798-9106","authenticated-orcid":false,"given":"Jaewoo","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Korea University , Seoul 02841, Republic of Korea"},{"name":"AIGEN Sciences , Seoul 04778, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"2023063008155037900_btad207-B1","doi-asserted-by":"crossref","first-page":"W530","DOI":"10.1093\/nar\/gkab294","article-title":"Plip 2021: expanding the scope of the protein\u2013ligand interaction profiler to DNA and RNA","volume":"49","author":"Adasme","year":"2021","journal-title":"Nucleic Acids Res"},{"key":"2023063008155037900_btad207-B2","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s10930-020-09884-2","article-title":"Covalent versus non-covalent enzyme inhibition: which route should we take? A justification of the good and bad from molecular modelling perspective","volume":"39","author":"Aljoundi","year":"2020","journal-title":"Protein J"},{"key":"2023063008155037900_btad207-B3","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/978-1-0716-0282-9_5","article-title":"Underappreciated chemical interactions in protein\u2013ligand complexes","volume-title":"Quantum Mechanics in Drug Discovery","author":"Anighoro","year":"2020"},{"key":"2023063008155037900_btad207-B4","author":"Ba","year":"2016"},{"key":"2023063008155037900_btad207-B5","author":"Bahdanau","year":"2014"},{"key":"2023063008155037900_btad207-B6","doi-asserted-by":"crossref","first-page":"2887","DOI":"10.1021\/jm9602928","article-title":"The properties of known drugs. 1. molecular frameworks","volume":"39","author":"Bemis","year":"1996","journal-title":"J Med Chem"},{"key":"2023063008155037900_btad207-B7","doi-asserted-by":"crossref","first-page":"D488","DOI":"10.1093\/nar\/gkac1077","article-title":"Rcsb protein data bank (rcsb. org): delivery of experimentally-determined pdb structures alongside one million computed structure models of proteins from artificial intelligence\/machine learning","volume":"51","author":"Burley","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2023063008155037900_btad207-B8","doi-asserted-by":"crossref","first-page":"703","DOI":"10.3389\/fchem.2019.00703","article-title":"Advances in ms based strategies for probing ligand-target interactions: focus on soft ionization mass spectrometric techniques","volume":"7","author":"Chen","year":"2019","journal-title":"Front Chem"},{"key":"2023063008155037900_btad207-B9","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1016\/j.drudis.2018.01.039","article-title":"The rise of deep learning in drug discovery","volume":"23","author":"Chen","year":"2018","journal-title":"Drug Discov Today"},{"key":"2023063008155037900_btad207-B10","doi-asserted-by":"crossref","first-page":"4406","DOI":"10.1093\/bioinformatics\/btaa524","article-title":"Transformercpi: improving compound\u2013protein 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":"2023063008155037900_btad207-B11","doi-asserted-by":"crossref","first-page":"4153","DOI":"10.1093\/bioinformatics\/btac485","article-title":"Iifdti: predicting drug\u2013target interactions through interactive and independent features based on attention mechanism","volume":"38","author":"Cheng","year":"2022","journal-title":"Bioinformatics"},{"key":"2023063008155037900_btad207-B12","first-page":"357","author":"Choe","year":"2022"},{"key":"2023063008155037900_btad207-B13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-04590-0","article-title":"A review of some techniques for inclusion of domain-knowledge into deep neural networks","volume":"12","author":"Dash","year":"2022","journal-title":"Sci Rep"},{"key":"2023063008155037900_btad207-B14","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1039\/C6SC04157D","article-title":"Harnessing non-covalent interactions to exert control over regioselectivity and site-selectivity in catalytic reactions","volume":"8","author":"Davis","year":"2017","journal-title":"Chem Sci"},{"key":"2023063008155037900_btad207-B15","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1038\/nbt.1990","article-title":"Comprehensive analysis of kinase inhibitor selectivity","volume":"29","author":"Davis","year":"2011","journal-title":"Nat Biotechnol"},{"key":"2023063008155037900_btad207-B16","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.2174\/1568026615666150915111741","article-title":"Sulfur containing scaffolds in drugs: synthesis and application in medicinal chemistry","volume":"16","author":"Feng","year":"2016","journal-title":"Curr Top Med Chem"},{"key":"2023063008155037900_btad207-B17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13321-017-0209-z","article-title":"Simboost: a read-across approach for predicting drug\u2013target binding affinities using gradient boosting machines","volume":"9","author":"He","year":"2017","journal-title":"J Cheminform"},{"key":"2023063008155037900_btad207-B18","doi-asserted-by":"crossref","first-page":"134","DOI":"10.3390\/molecules23010134","article-title":"Synthesis and pharmacological activities of pyrazole derivatives: a review","volume":"23","author":"Karrouchi","year":"2018","journal-title":"Molecules"},{"key":"2023063008155037900_btad207-B19","doi-asserted-by":"crossref","first-page":"D1373","DOI":"10.1093\/nar\/gkac956","article-title":"Pubchem 2023 update","volume":"51","author":"Kim","year":"2023","journal-title":"Nucleic Acids Res"},{"key":"2023063008155037900_btad207-B20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13065-018-0406-5","article-title":"Therapeutic potential of heterocyclic pyrimidine scaffolds","volume":"12","author":"Kumar","year":"2018","journal-title":"Chem Central J"},{"key":"2023063008155037900_btad207-B21","first-page":"3744","author":"Lee","year":"2019"},{"key":"2023063008155037900_btad207-B22","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.1093\/bioinformatics\/btac035","article-title":"Bacpi: a bi-directional attention neural network for compound\u2013protein interaction and binding affinity prediction","volume":"38","author":"Li","year":"2022","journal-title":"Bioinformatics"},{"key":"2023063008155037900_btad207-B23","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"},{"key":"2023063008155037900_btad207-B24","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.neucom.2020.08.011","article-title":"Explaining the black-box model: a survey of local interpretation methods for deep neural networks","volume":"419","author":"Liang","year":"2021","journal-title":"Neurocomputing"},{"key":"2023063008155037900_btad207-B25","author":"Lin","year":"2022"},{"key":"2023063008155037900_btad207-B26","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1021\/acs.accounts.6b00491","article-title":"Forging the basis for developing protein\u2013ligand interaction scoring functions","volume":"50","author":"Liu","year":"2017","journal-title":"Acc Chem Res"},{"key":"2023063008155037900_btad207-B27","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1038\/nchembio.530","article-title":"Navigating the kinome","volume":"7","author":"Metz","year":"2011","journal-title":"Nat Chem Biol"},{"key":"2023063008155037900_btad207-B28","doi-asserted-by":"crossref","first-page":"1140","DOI":"10.1093\/bioinformatics\/btaa921","article-title":"Graphdta: predicting drug\u2013target binding affinity with graph neural networks","volume":"37","author":"Nguyen","year":"2021","journal-title":"Bioinformatics"},{"key":"2023063008155037900_btad207-B29","doi-asserted-by":"crossref","first-page":"i821","DOI":"10.1093\/bioinformatics\/bty593","article-title":"Deepdta: deep drug\u2013target binding affinity prediction","volume":"34","author":"\u00d6zt\u00fcrk","year":"2018","journal-title":"Bioinformatics"},{"key":"2023063008155037900_btad207-B30","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1093\/bib\/bbu010","article-title":"Toward more realistic drug\u2013target interaction predictions","volume":"16","author":"Pahikkala","year":"2015","journal-title":"Brief Bioinform"},{"key":"2023063008155037900_btad207-B31","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/978-3-030-28954-6_18","volume-title":"Explainable AI: Interpreting, Explaining and Visualizing Deep Learning","author":"Preuer","year":"2019"},{"key":"2023063008155037900_btad207-B32","author":"Rives","year":"2019"},{"key":"2023063008155037900_btad207-B33","first-page":"804","article-title":"The identification of potent, selective, and brain penetrant pi5p4k\u03b3 inhibitors as in vivo-ready tool molecules","author":"Rooney","year":"2022","journal-title":"J Med Chem"},{"key":"2023063008155037900_btad207-B34","author":"Schr\u00f6dinger","year":"2020"},{"key":"2023063008155037900_btad207-B35","doi-asserted-by":"crossref","first-page":"960","DOI":"10.2741\/4527","article-title":"Understanding ligand-receptor non-covalent binding kinetics using molecular modeling","volume":"22","author":"Tang","year":"2017","journal-title":"Front Biosci (Landmark Ed)"},{"key":"2023063008155037900_btad207-B36","article-title":"Attention is all you need","volume-title":"Advances in Neural Information Processing Systems","author":"Vaswani","year":"2017"},{"key":"2023063008155037900_btad207-B37","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1109\/TCBB.2022.3170365","article-title":"Attentiondta: drug-target binding affinity prediction by sequence-based deep learning with attention mechanism","volume":"20","author":"Zhao","year":"2022","journal-title":"IEEE\/ACM Trans Comput Biol and Bioinf"},{"key":"2023063008155037900_btad207-B38","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"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/Supplement_1\/i448\/50741623\/btad207.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/Supplement_1\/i448\/50741623\/btad207.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T08:19:05Z","timestamp":1688113145000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/39\/Supplement_1\/i448\/7210465"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,1]]},"references-count":38,"journal-issue":{"issue":"Supplement_1","published-print":{"date-parts":[[2023,6,30]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btad207","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,6,1]]},"published":{"date-parts":[[2023,6,1]]}}}