{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T02:06:19Z","timestamp":1779156379224,"version":"3.51.4"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":40,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100004410","name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu","doi-asserted-by":"publisher","award":["121E208"],"award-info":[{"award-number":["121E208"]}],"id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"DOI":"10.1186\/s12859-026-06409-z","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T07:28:05Z","timestamp":1775806085000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Molecular contrastive learning with graph attention network (MoCL-GAT) for enhanced molecular representation"],"prefix":"10.1186","volume":"27","author":[{"given":"Alperen","family":"Dalk\u0131ran","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6717-4767","authenticated-orcid":false,"given":"Ahmet Sureyya","family":"Rifaioglu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rengul","family":"Cetin-Atalay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aybar C.","family":"Acar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tunca","family":"Do\u011fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. Volkan","family":"Atalay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,9]]},"reference":[{"key":"6409_CR1","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1038\/nrd.2017.232","volume":"17","author":"G Schneider","year":"2018","unstructured":"Schneider G. Automating drug discovery. Nat Rev Drug Discov. 2018;17:97\u2013113.","journal-title":"Nat Rev Drug Discov"},{"key":"6409_CR2","doi-asserted-by":"publisher","first-page":"1878","DOI":"10.1093\/bib\/bby061","volume":"20","author":"AS Rifaioglu","year":"2019","unstructured":"Rifaioglu AS, Atas H, Martin MJ, Cetin-Atalay R, Atalay V, Do\u011fan T. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief Bioinform. 2019;20:1878\u2013912. https:\/\/doi.org\/10.1093\/bib\/bby061.","journal-title":"Brief Bioinform"},{"key":"6409_CR3","doi-asserted-by":"publisher","first-page":"2531","DOI":"10.1039\/C9SC03414E","volume":"11","author":"AS Rifaioglu","year":"2020","unstructured":"Rifaioglu AS, Nalbat E, Atalay V, Martin MJ, Cetin-Atalay R, Do\u011fan T. DEEPScreen: high performance drug\u2013target interaction prediction with convolutional neural networks using 2-D structural compound representations. Chem Sci. 2020;11:2531\u201357. https:\/\/doi.org\/10.1039\/C9SC03414E.","journal-title":"Chem Sci"},{"key":"6409_CR4","doi-asserted-by":"publisher","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y. Graph attention networks. 2018. https:\/\/doi.org\/10.48550\/arXiv.1710.10903.","DOI":"10.48550\/arXiv.1710.10903"},{"key":"6409_CR5","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btad234","author":"A Dalk\u0131ran","year":"2023","unstructured":"Dalk\u0131ran A, Atakan A, Rifaio\u011flu AS, Martin MJ, Atalay R\u00c7, Acar AC, et al. Transfer learning for drug\u2013target interaction prediction. Bioinformatics. 2023. https:\/\/doi.org\/10.1093\/bioinformatics\/btad234.","journal-title":"Bioinformatics"},{"key":"6409_CR6","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3090866","author":"X Liu","year":"2021","unstructured":"Liu X, Zhang F, Hou Z, Mian L, Wang Z, Zhang J, et al. Self-supervised learning: generative or contrastive. IEEE Trans Knowl Data Eng. 2021. https:\/\/doi.org\/10.1109\/TKDE.2021.3090866.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"6409_CR7","first-page":"12559","volume-title":"Advances in neural information processing systems","author":"Y Rong","year":"2020","unstructured":"Rong Y, Bian Y, Xu T, Xie W, Wei Y, Huang W, et al. Self-supervised graph transformer on large-scale molecular data. In: Larochelle H, Ranzato M, Hadsell R, Balcan MF, Lin H, editors., et al., Advances in neural information processing systems. New York: Curran Associates, Inc.; 2020. p. 12559\u201371."},{"key":"6409_CR8","doi-asserted-by":"publisher","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, et al. RoBERTa: a robustly optimized BERT pretraining approach. 2019. https:\/\/doi.org\/10.48550\/arXiv.1907.11692.","DOI":"10.48550\/arXiv.1907.11692"},{"key":"6409_CR9","doi-asserted-by":"publisher","unstructured":"Fabian B, Edlich T, Gaspar H, Segler M, Meyers J, Fiscato M, et al. Molecular representation learning with language models and domain-relevant auxiliary tasks. 2020. https:\/\/doi.org\/10.48550\/arXiv.2011.13230.","DOI":"10.48550\/arXiv.2011.13230"},{"key":"6409_CR10","doi-asserted-by":"publisher","unstructured":"Ahmad W, Simon E, Chithrananda S, Grand G, Ramsundar B. ChemBERTa-2: towards chemical foundation models. 2022. https:\/\/doi.org\/10.48550\/arXiv.2209.01712.","DOI":"10.48550\/arXiv.2209.01712"},{"key":"6409_CR11","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/acdb30","volume":"4","author":"A Y\u00fcksel","year":"2023","unstructured":"Y\u00fcksel A, Ulusoy E, \u00dcnl\u00fc A, Do\u011fan T. SELFormer: molecular representation learning via SELFIES language models. Mach Learn Sci Technol. 2023;4:025035. https:\/\/doi.org\/10.1088\/2632-2153\/acdb30.","journal-title":"Mach Learn Sci Technol"},{"key":"6409_CR12","doi-asserted-by":"publisher","unstructured":"Hu W, Liu B, Gomes J, Zitnik M, Liang P, Pande V, et al. Strategies for pre-training graph neural networks. 2020. https:\/\/doi.org\/10.48550\/arXiv.1905.12265.","DOI":"10.48550\/arXiv.1905.12265"},{"key":"6409_CR13","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1038\/s42256-022-00447-x","volume":"4","author":"Y Wang","year":"2022","unstructured":"Wang Y, Wang J, Cao Z, Barati Farimani A. Molecular contrastive learning of representations via graph neural networks. Nat Mach Intell. 2022;4:279\u201387. https:\/\/doi.org\/10.1038\/s42256-022-00447-x.","journal-title":"Nat Mach Intell"},{"key":"6409_CR14","doi-asserted-by":"publisher","unstructured":"Liu S, Wang H, Liu W, Lasenby J, Guo H, Tang J. Pre-training molecular graph representation with 3D geometry. 2022. https:\/\/doi.org\/10.48550\/arXiv.2110.07728.","DOI":"10.48550\/arXiv.2110.07728"},{"key":"6409_CR15","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1038\/s42256-021-00438-4","volume":"4","author":"X Fang","year":"2022","unstructured":"Fang X, Liu L, Lei J, He D, Zhang S, Zhou J, et al. Geometry-enhanced molecular representation learning for property prediction. Nat Mach Intell. 2022;4:127\u201334. https:\/\/doi.org\/10.1038\/s42256-021-00438-4.","journal-title":"Nat Mach Intell"},{"key":"6409_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102784","volume":"115","author":"R Chen","year":"2025","unstructured":"Chen R, Li C, Wang L, Liu M, Chen S, Yang J, et al. Pretraining graph transformer for molecular representation with fusion of multimodal information. Inf Fusion. 2025;115:102784. https:\/\/doi.org\/10.1016\/j.inffus.2024.102784.","journal-title":"Inf Fusion"},{"key":"6409_CR17","unstructured":"Sch\u00fctt K, Kindermans P-J, Felix HES, Chmiela S, Tkatchenko A, M\u00fcller K-R. SchNet: a continuous-filter convolutional neural network for modeling quantum interactions. Adv Neural Inf Process Syst. 2017;30."},{"key":"6409_CR18","doi-asserted-by":"publisher","first-page":"1052","DOI":"10.1609\/aaai.v33i01.33011052","volume":"33","author":"C Lu","year":"2019","unstructured":"Lu C, Liu Q, Wang C, Huang Z, Lin P, He L. Molecular property prediction: a multilevel quantum interactions modeling perspective. Proc AAAI Conf Artif Intell. 2019;33:1052\u201360. https:\/\/doi.org\/10.1609\/aaai.v33i01.33011052.","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"6409_CR19","doi-asserted-by":"publisher","unstructured":"Li H, Zhao D, Zeng J. KPGT: knowledge-guided pre-training of graph transformer for molecular property prediction. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining. Washington DC USA: ACM; 2022. p. 857\u201367. https:\/\/doi.org\/10.1145\/3534678.3539426.","DOI":"10.1145\/3534678.3539426"},{"key":"6409_CR20","doi-asserted-by":"publisher","first-page":"2713","DOI":"10.1021\/acs.jcim.2c00495","volume":"62","author":"Y Wang","year":"2022","unstructured":"Wang Y, Magar R, Liang C, Barati Farimani A. Improving molecular contrastive learning via faulty negative mitigation and decomposed fragment contrast. J Chem Inf Model. 2022;62:2713\u201325. https:\/\/doi.org\/10.1021\/acs.jcim.2c00495.","journal-title":"J Chem Inf Model"},{"key":"6409_CR21","doi-asserted-by":"publisher","unstructured":"Wang H, Li W, Jin X, Cho K, Ji H, Han J, et al. Chemical-reaction-aware molecule representation learning. 2021. https:\/\/doi.org\/10.48550\/arXiv.2109.09888.","DOI":"10.48550\/arXiv.2109.09888"},{"key":"6409_CR22","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaf355","volume":"41","author":"A Li","year":"2025","unstructured":"Li A, Casiraghi E, Rousu J. CSGL: chemical synthesis graph learning for molecule representation. Bioinformatics. 2025;41:btaf355. https:\/\/doi.org\/10.1093\/bioinformatics\/btaf355.","journal-title":"Bioinformatics"},{"key":"6409_CR23","unstructured":"Landrum G. RDKit: Open-source cheminformatics. https:\/\/www.rdkit.org. 2006."},{"key":"6409_CR24","doi-asserted-by":"publisher","unstructured":"Oord A van den, Li Y, Vinyals O. Representation learning with contrastive predictive coding. 2019. https:\/\/doi.org\/10.48550\/arXiv.1807.03748.","DOI":"10.48550\/arXiv.1807.03748"},{"key":"6409_CR25","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-1-8","volume":"1","author":"P Ertl","year":"2009","unstructured":"Ertl P, Schuffenhauer A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminform. 2009;1:8. https:\/\/doi.org\/10.1186\/1758-2946-1-8.","journal-title":"J Cheminform"},{"key":"6409_CR26","doi-asserted-by":"publisher","first-page":"D1100","DOI":"10.1093\/nar\/gkr777","volume":"40","author":"A Gaulton","year":"2012","unstructured":"Gaulton A, Bellis LJ, Bento AP, Chambers J, Davies M, Hersey A, et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2012;40:D1100\u20137. https:\/\/doi.org\/10.1093\/nar\/gkr777.","journal-title":"Nucleic Acids Res"},{"key":"6409_CR27","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1039\/C7SC02664A","volume":"9","author":"Z Wu","year":"2018","unstructured":"Wu Z, Ramsundar B, Feinberg EN, Gomes J, Geniesse C, Pappu AS, et al. MoleculeNet: a benchmark for molecular machine learning. Chem Sci. 2018;9:513\u201330. https:\/\/doi.org\/10.1039\/C7SC02664A.","journal-title":"Chem Sci"},{"key":"6409_CR28","doi-asserted-by":"publisher","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et al. PyTorch: An imperative style, high-performance deep learning library. 2019. https:\/\/doi.org\/10.48550\/arXiv.1912.01703.","DOI":"10.48550\/arXiv.1912.01703"},{"key":"6409_CR29","doi-asserted-by":"publisher","unstructured":"Fey M, Lenssen JE. Fast graph representation learning with PyTorch geometric. 2019. https:\/\/doi.org\/10.48550\/arXiv.1903.02428.","DOI":"10.48550\/arXiv.1903.02428"},{"issue":"11","key":"6409_CR30","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten L, Hinton G. Visualizing data using t-SNE. J Mach Learn Res. 2008;9(11):2579\u2013605.","journal-title":"J Mach Learn Res"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-026-06409-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-026-06409-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-026-06409-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T01:11:46Z","timestamp":1779153106000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12859-026-06409-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,9]]},"references-count":30,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["6409"],"URL":"https:\/\/doi.org\/10.1186\/s12859-026-06409-z","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,9]]},"assertion":[{"value":"10 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"106"}}