{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T16:37:21Z","timestamp":1787503041367,"version":"build-2736575974"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T00:00:00Z","timestamp":1775779200000},"content-version":"vor","delay-in-days":37,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100019904","name":"Vellore Institute of Technology, Chennai","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100019904","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"DOI":"10.1186\/s12859-026-06405-3","type":"journal-article","created":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T10:55:09Z","timestamp":1772621709000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Uncertainty-aware hybrid deep generative framework for robust and explainable drug discovery"],"prefix":"10.1186","volume":"27","author":[{"given":"Saniya","family":"Gupta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. Sherly","family":"Alphonse","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D.","family":"Kavitha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,4]]},"reference":[{"key":"6405_CR1","doi-asserted-by":"publisher","unstructured":"Bahi M, Batouche M. Drug-target interaction prediction in drug repositioning based on deep semi-supervised learning. In: Computational intelligence and its applications, volume 522 of IFIP advances in information and communication technology. Cham: Springer International Publishing; 2018. p. 302\u201313. https:\/\/doi.org\/10.1007\/978-3-319-89743-1_27.","DOI":"10.1007\/978-3-319-89743-1_27"},{"key":"6405_CR2","unstructured":"Bakal G, Kilicoglu H, Kavuluru R. Non-negative matrix factorization for drug repositioning: experiments with the repodb dataset. In: AMIA Annual Symposium Proceedings 2019;238\u2013247:2019."},{"key":"6405_CR3","doi-asserted-by":"publisher","unstructured":"Belakaria S, Deshwal A, Jayakodi NK, Doppa JR. Uncertainty-aware search framework for multi-objective bayesian optimization. In: Proceedings of the AAAI Conference on Artificial Intelligence 2020;34:10044\u201352. https:\/\/doi.org\/10.1609\/aaai.v34i06.6561.","DOI":"10.1609\/aaai.v34i06.6561"},{"issue":"2","key":"6405_CR4","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/nchem.1243","volume":"4","author":"GR Bickerton","year":"2012","unstructured":"Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL. Quantifying the chemical beauty of drugs. Nat Chem. 2012;4(2):90\u20138. https:\/\/doi.org\/10.1038\/nchem.1243.","journal-title":"Nat Chem"},{"issue":"1","key":"6405_CR5","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1186\/s12859-023-05286-0","volume":"24","author":"L Chen","year":"2023","unstructured":"Chen L, Shen Q, Lou J. Magicmol: a light-weighted pipeline for drug-like molecule evolution and quick chemical space exploration. BMC Bioinform. 2023;24(1):173. https:\/\/doi.org\/10.1186\/s12859-023-05286-0.","journal-title":"BMC Bioinform"},{"key":"6405_CR6","doi-asserted-by":"publisher","unstructured":"Cao ND, Kipf T. Molgan: an implicit generative model for small molecular graphs. In: ICML 2018 workshop on theoretical foundations and applications of deep generative models, 2018. https:\/\/doi.org\/10.48550\/arXiv.1805.11973.","DOI":"10.48550\/arXiv.1805.11973"},{"issue":"29","key":"6405_CR7","doi-asserted-by":"publisher","first-page":"13352","DOI":"10.1039\/D4SC08736D","volume":"16","author":"T Dong","year":"2025","unstructured":"Dong T, You L, Chen CY. Multi-objective drug design with a scaffold-aware variational autoencoder. Chem Sci. 2025;16(29):13352. https:\/\/doi.org\/10.1039\/D4SC08736D.","journal-title":"Chem Sci"},{"issue":"1","key":"6405_CR8","doi-asserted-by":"publisher","first-page":"8","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(1):8. https:\/\/doi.org\/10.1186\/1758-2946-1-8.","journal-title":"J Cheminform"},{"issue":"2","key":"6405_CR9","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","volume":"4","author":"R G\u00f3mez-Bombarelli","year":"2018","unstructured":"G\u00f3mez-Bombarelli R, Wei JN, Duvenaud D, Hern\u00e1ndez-Lobato JM, S\u00e1nchez-Lengeling B, Sheberla D, et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci. 2018;4(2):268\u201376. https:\/\/doi.org\/10.1021\/acscentsci.7b00572.","journal-title":"ACS Cent Sci"},{"issue":"1","key":"6405_CR10","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1186\/s12859-020-03898-4","volume":"21","author":"J He","year":"2020","unstructured":"He J, Yang X, Gong Z, Zamit L. Hybrid attentional memory network for computational drug repositioning. BMC Bioinform. 2020;21(1):566.","journal-title":"BMC Bioinform"},{"issue":"1","key":"6405_CR11","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1021\/ci049714+","volume":"45","author":"JJ Irwin","year":"2005","unstructured":"Irwin JJ, Shoichet BK. Zinc-a free database of commercially available compounds for virtual screening. J Chem Inf Model. 2005;45(1):177\u201382. https:\/\/doi.org\/10.1021\/ci049714+.","journal-title":"J Chem Inf Model"},{"issue":"8","key":"6405_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2021.100307","volume":"2","author":"S Jin","year":"2021","unstructured":"Jin S, Niu Z, Jiang C, Huang W, Xia F, Jin X, et al. Hetdr: Drug repositioning based on heterogeneous networks and text mining. Patterns. 2021;2(8):100307. https:\/\/doi.org\/10.1016\/j.patter.2021.100307.","journal-title":"Patterns"},{"key":"6405_CR13","doi-asserted-by":"publisher","unstructured":"Jin W, Barzilay R, Jaakkola T. Junction tree variational autoencoder for molecular graph generation. In: Proceedings of the 35th international conference on machine learning, volume 80 of proceedings of machine learning research, 2018, p. 2323\u20132332. https:\/\/doi.org\/10.48550\/arXiv.1802.04364.","DOI":"10.48550\/arXiv.1802.04364"},{"key":"6405_CR14","doi-asserted-by":"publisher","unstructured":"Kipf TN, Welling M. Semi-supervised classification with graph convolutional networks. In: International conference on learning representations (ICLR), 2017. https:\/\/doi.org\/10.48550\/arXiv.1609.02907","DOI":"10.48550\/arXiv.1609.02907"},{"key":"6405_CR15","doi-asserted-by":"publisher","unstructured":"Li P, Tuzhilin A. Deep pareto reinforcement learning for multi-objective recommender systems, 2024. arXiv preprint arXiv:2407.03580. https:\/\/doi.org\/10.48550\/arXiv.2407.03580.","DOI":"10.48550\/arXiv.2407.03580"},{"key":"6405_CR16","unstructured":"Liu S, Qu M, Zhang Z, Cai H, Tang J. Structured multi-task learning for molecular property prediction. In: Proceedings of the 25th international conference on artificial intelligence and statistics (aistats), volume 151 of proceedings of machine learning research, p. 8906\u20138920, 2022. https:\/\/proceedings.mlr.press\/v151\/liu22e.html."},{"issue":"3","key":"6405_CR17","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1093\/bioinformatics\/btu626","volume":"31","author":"Z Liu","year":"2015","unstructured":"Liu Z, Li Y, Han L, Li J, Liu J, Zhao Z, et al. Pdb-wide collection of binding data: current status of the pdbbind database. Bioinformatics. 2015;31(3):405\u201312. https:\/\/doi.org\/10.1093\/bioinformatics\/btu626.","journal-title":"Bioinformatics"},{"issue":"11","key":"6405_CR18","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1093\/bioinformatics\/bty013","volume":"34","author":"H Luo","year":"2018","unstructured":"Luo H, Li M, Wang S, Liu Q, Li Y, Wang J. Computational drug repositioning using low-rank matrix approximation and randomized algorithms. Bioinformatics. 2018;34(11):1904\u201312. https:\/\/doi.org\/10.1093\/bioinformatics\/bty013.","journal-title":"Bioinformatics"},{"key":"6405_CR19","doi-asserted-by":"publisher","unstructured":"Luo Y, Yan K, Ji S. Graphdf: A discrete flow model for molecular graph generation. In: Proceedings of the 38th international conference on machine learning, volume 139 of proceedings of machine learning research, p. 7192\u20137203, 2021. https:\/\/doi.org\/10.48550\/arXiv.2102.01189.","DOI":"10.48550\/arXiv.2102.01189"},{"key":"6405_CR20","doi-asserted-by":"publisher","unstructured":"Musial S, Zielinski B, Danel T. Fragment-wise interpretability in graph neural networks via molecule decomposition and contribution analysis. arXiv preprint arXiv:2508.15015, 2025. https:\/\/doi.org\/10.48550\/arXiv.2508.15015.","DOI":"10.48550\/arXiv.2508.15015"},{"issue":"1","key":"6405_CR21","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s13321-017-0235-x","volume":"9","author":"M Olivecrona","year":"2017","unstructured":"Olivecrona M, Blaschke T, Engkvist O, Chen H. Molecular de-novo design through deep reinforcement learning. J Cheminform. 2017;9(1):48. https:\/\/doi.org\/10.1186\/s13321-017-0235-x.","journal-title":"J Cheminform"},{"key":"6405_CR22","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2020.565644","volume":"11","author":"D Polykovskiy","year":"2020","unstructured":"Polykovskiy D, Zhebrak A, Sanchez-Lengeling B, Golovanov S, Tatanov O, Belyaev S, et al. Molecular sets (MOSES): a benchmarking platform for molecular generation models. Front Pharmacol. 2020;11:565644. https:\/\/doi.org\/10.3389\/fphar.2020.565644.","journal-title":"Front Pharmacol"},{"issue":"7","key":"6405_CR23","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aap7885","volume":"4","author":"M Popova","year":"2018","unstructured":"Popova M, Isayev O, Tropsha A. Deep reinforcement learning for de novo drug design. Sci Adv. 2018;4(7):eaab7885. https:\/\/doi.org\/10.1126\/sciadv.aap7885.","journal-title":"Sci Adv"},{"key":"6405_CR24","doi-asserted-by":"publisher","first-page":"4382","DOI":"10.1038\/s41467-025-59634-0","volume":"16","author":"J Qiao","year":"2025","unstructured":"Qiao J, Jin J, Wang D, Teng S, Zhang J, Yang X, et al. A self-conformation-aware pre-training framework for molecular property prediction with substructure interpretability. Nat Commun. 2025;16:4382. https:\/\/doi.org\/10.1038\/s41467-025-59634-0.","journal-title":"Nat Commun"},{"issue":"5","key":"6405_CR25","doi-asserted-by":"publisher","first-page":"1369","DOI":"10.1093\/bioinformatics\/btab826","volume":"38","author":"S Sadeghi","year":"2022","unstructured":"Sadeghi S, Jianguo L, Ngom A. A network-based drug repurposing method via non-negative matrix factorization. Bioinformatics. 2022;38(5):1369\u201377. https:\/\/doi.org\/10.1093\/bioinformatics\/btab826.","journal-title":"Bioinformatics"},{"issue":"7698","key":"6405_CR26","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1038\/nature25978","volume":"555","author":"MH Segler","year":"2018","unstructured":"Segler MH, Preuss M, Waller MP. Planning chemical syntheses with deep neural networks and symbolic AI. Nature. 2018;555(7698):604\u201310. https:\/\/doi.org\/10.1038\/nature25978.","journal-title":"Nature"},{"key":"6405_CR27","unstructured":"Shi C, Xu M, Zhu Z, Zhang W, Zhang M, Tang J. Graphaf: a flow-based autoregressive model for molecular graph generation. In: international conference on learning representations (ICLR), 2020. https:\/\/doi.org\/10.48550\/arXiv.2001.09382"},{"key":"6405_CR28","unstructured":"Simm G, Pinsler R, Hern\u00e1ndez-Lobato JM. Reinforcement learning for molecular design guided by quantum mechanics. In: Proceedings of the 37th international conference on machine learning, volume 119 of proceedings of machine learning research, p. 8959\u20138969. PMLR, 2020."},{"issue":"3","key":"6405_CR29","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1109\/TEVC.2016.2608507","volume":"21","author":"A Trivedi","year":"2017","unstructured":"Trivedi A, Srinivasan D, Sanyal K, Ghosh A. A survey of multiobjective evolutionary algorithms based on decomposition. IEEE Trans Evol Comput. 2017;21(3):440\u201362. https:\/\/doi.org\/10.1109\/TEVC.2016.2608507.","journal-title":"IEEE Trans Evol Comput"},{"key":"6405_CR30","unstructured":"Tuo R, Wang W. Uncertainty quantification for bayesian optimization. In: Proceedings of the 25th international conference on artificial intelligence and statistics, volume 151 of proceedings of machine learning research, pages 2862\u20132884. PMLR, 2022. URL https:\/\/proceedings.mlr.press\/v151\/tuo22a.html."},{"issue":"12","key":"6405_CR31","doi-asserted-by":"publisher","first-page":"2977","DOI":"10.1021\/jm030580l","volume":"47","author":"R Wang","year":"2004","unstructured":"Wang R, Fang X, Yipin L, Wang S. The pdbbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. J Med Chem. 2004;47(12):2977\u201380. https:\/\/doi.org\/10.1021\/jm030580l.","journal-title":"J Med Chem"},{"issue":"20","key":"6405_CR32","doi-asserted-by":"publisher","first-page":"2923","DOI":"10.1093\/bioinformatics\/btu403","volume":"30","author":"W Wang","year":"2014","unstructured":"Wang W, Yang S, Zhang X, Li J. Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics. 2014;30(20):2923\u201330. https:\/\/doi.org\/10.1093\/bioinformatics\/btu403.","journal-title":"Bioinformatics"},{"issue":"8","key":"6405_CR33","doi-asserted-by":"publisher","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","volume":"59","author":"K Yang","year":"2019","unstructured":"Yang K, Swanson K, Jin W, Coley C, Eiden P, Gao H, et al. Analyzing learned molecular representations for property prediction. J Chem Inf Model. 2019;59(8):3370\u201388. https:\/\/doi.org\/10.1021\/acs.jcim.9b00237.","journal-title":"J Chem Inf Model"},{"issue":"1","key":"6405_CR34","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1186\/s12859-019-2983-2","volume":"20","author":"X Yang","year":"2019","unstructured":"Yang X, Zamit I, Liu Y, He J. Additional neural matrix factorization model for computational drug repositioning. BMC Bioinform. 2019;20(1):423. https:\/\/doi.org\/10.1186\/s12859-019-2983-2.","journal-title":"BMC Bioinform"},{"issue":"2","key":"6405_CR35","doi-asserted-by":"publisher","first-page":"1506","DOI":"10.1109\/TCBB.2022.3212051","volume":"20","author":"X Yang","year":"2023","unstructured":"Yang X, Yang G, Chu J. The computational drug repositioning without negative sampling. IEEE\/ACM Trans Comput Biol Bioinf. 2023;20(2):1506\u201317. https:\/\/doi.org\/10.1109\/TCBB.2022.3212051.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"5","key":"6405_CR36","doi-asserted-by":"publisher","first-page":"3245","DOI":"10.1109\/TCBB.2023.3254163","volume":"20","author":"X Yang","year":"2023","unstructured":"Yang X, Yang G, Chu J. Self-supervised learning for label sparsity in computational drug repositioning. IEEE\/ACM Trans Comput Biol Bioinf. 2023;20(5):3245\u201356. https:\/\/doi.org\/10.1109\/TCBB.2023.3254163.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"8","key":"6405_CR37","doi-asserted-by":"publisher","first-page":"4544","DOI":"10.1109\/JBHI.2024.3350666","volume":"28","author":"X Yang","year":"2024","unstructured":"Yang X, Yang G, Chu J. Graphcl-dta: A graph contrastive learning with molecular semantics for drug-target binding affinity prediction. IEEE J Biomed Health Inform. 2024;28(8):4544\u201352. https:\/\/doi.org\/10.1109\/JBHI.2024.3350666.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"9","key":"6405_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.xgen.2025.100950","volume":"5","author":"X Yang","year":"2025","unstructured":"Yang X, Zhao F, Ren T, Chen C, Byrne KT, Danilov AV, et al. Omicstweezer: A distribution-independent cell deconvolution model for multi-omics data. Cell Genomics. 2025;5(9):100950. https:\/\/doi.org\/10.1016\/j.xgen.2025.100950.","journal-title":"Cell Genomics"},{"key":"6405_CR39","doi-asserted-by":"publisher","first-page":"2409","DOI":"10.1038\/s41467-025-52420-5","volume":"16","author":"T Yoshizawa","year":"2025","unstructured":"Yoshizawa T, Ishida S, Sato T, Ohta M, Honma T, Terayama K. A data-driven generative strategy to avoid reward hacking in multi-objective molecular design. Nat Commun. 2025;16:2409. https:\/\/doi.org\/10.1038\/s41467-025-52420-5.","journal-title":"Nat Commun"},{"key":"6405_CR40","unstructured":"You J, Liu B, Ying R, Pande V, Leskovec J. Graph convolutional policy network for goal-directed molecular graph generation. In: Advances in neural information processing systems (NeurIPS), 2018. https:\/\/doi.org\/10.48550\/arXiv.1806.02473"},{"key":"6405_CR41","doi-asserted-by":"publisher","unstructured":"Zang C, Wang F. Moflow: an invertible flow model for generating molecular graphs. In: Proceedings of the 26th acm sigkdd international conference on knowledge discovery and data mining, p. 617\u2013626, 2020. https:\/\/doi.org\/10.1145\/3394486.3403104.","DOI":"10.1145\/3394486.3403104"},{"issue":"D1","key":"6405_CR42","doi-asserted-by":"publisher","first-page":"D1180","DOI":"10.1093\/nar\/gkad1004","volume":"52","author":"B Zdrazil","year":"2024","unstructured":"Zdrazil B, Felix E, Hunter F, Manners EJ, Blackshaw J, Corbett S, et al. The chembl database in 2023 a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Res. 2024;52(D1):D1180\u201392. https:\/\/doi.org\/10.1093\/nar\/gkad1004.","journal-title":"Nucleic Acids Res"},{"issue":"9","key":"6405_CR43","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1038\/s41587-019-0224-x","volume":"37","author":"A Zhavoronkov","year":"2019","unstructured":"Zhavoronkov A, Ivanenkov YA, Aliper A, Veselov MS, Aladinskiy VA, Aladinskaya AV, et al. Deep learning enables rapid identification of potent ddr1 kinase inhibitors. Nat Biotechnol. 2019;37(9):1038\u201340. https:\/\/doi.org\/10.1038\/s41587-019-0224-x.","journal-title":"Nat Biotechnol"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-026-06405-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-026-06405-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-026-06405-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:41:31Z","timestamp":1775817691000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12859-026-06405-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,4]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["6405"],"URL":"https:\/\/doi.org\/10.1186\/s12859-026-06405-3","relation":{},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,4]]},"assertion":[{"value":"26 November 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"84"}}