{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T06:29:00Z","timestamp":1761719340633,"version":"3.40.4"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["JP22nk0101111"],"award-info":[{"award-number":["JP22nk0101111"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"DOI":"10.1186\/s13321-025-01015-2","type":"journal-article","created":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T11:02:50Z","timestamp":1746183770000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving the accuracy of prediction models for small datasets of Cytochrome P450 inhibition with deep learning"],"prefix":"10.1186","volume":"17","author":[{"given":"Elpri Eka","family":"Permadi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9359-8731","authenticated-orcid":false,"given":"Reiko","family":"Watanabe","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3021-7078","authenticated-orcid":false,"given":"Kenji","family":"Mizuguchi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,30]]},"reference":[{"key":"1015_CR1","doi-asserted-by":"publisher","first-page":"5059","DOI":"10.1021\/acs.jcim.1c00628","volume":"62","author":"T-H Nguyen-Vo","year":"2022","unstructured":"Nguyen-Vo T-H, Trinh QH, Nguyen L, Nguyen-Hoang P-U, Nguyen T-N, Nguyen DT, Nguyen BP, Le L (2022) iCYP-MFE: identifying human cytochrome P450 inhibitors using multitask learning and molecular fingerprint-embedded encoding. J Chem Inf Model 62:5059\u20135068. https:\/\/doi.org\/10.1021\/acs.jcim.1c00628","journal-title":"J Chem Inf Model"},{"key":"1015_CR2","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1080\/08998280.2000.11927719","volume":"13","author":"CC Ogu","year":"2000","unstructured":"Ogu CC, Maxa JL (2000) Drug interactions due to cytochrome P450. Bayl Univ Med Cent Proc 13:421\u2013423. https:\/\/doi.org\/10.1080\/08998280.2000.11927719","journal-title":"Bayl Univ Med Cent Proc"},{"key":"1015_CR3","doi-asserted-by":"publisher","first-page":"94","DOI":"10.3390\/jox11030007","volume":"11","author":"F Esteves","year":"2021","unstructured":"Esteves F, Rueff J, Kranendonk M (2021) The central role of cytochrome P450 in xenobiotic metabolism\u2014a brief review on a fascinating enzyme family. J Xenobiot 11:94\u2013114. https:\/\/doi.org\/10.3390\/jox11030007","journal-title":"J Xenobiot"},{"key":"1015_CR4","doi-asserted-by":"publisher","first-page":"1332","DOI":"10.1021\/acs.chemrestox.3c00065","volume":"36","author":"L Li","year":"2023","unstructured":"Li L, Lu Z, Liu G, Tang Y, Li W (2023) Machine Learning Models to Predict Cytochrome P450 2B6 Inhibitors and Substrates. Chem Res Toxicol 36:1332\u20131344. https:\/\/doi.org\/10.1021\/acs.chemrestox.3c00065","journal-title":"Chem Res Toxicol"},{"key":"1015_CR5","doi-asserted-by":"publisher","first-page":"1282","DOI":"10.1021\/acs.jcim.8b00035","volume":"58","author":"S Tian","year":"2018","unstructured":"Tian S, Djoumbou-Feunang Y, Greiner R, Wishart DS (2018) CypReact: a software tool for in silico reactant prediction for human cytochrome P450 enzymes. J Chem Inf Model 58:1282\u20131291. https:\/\/doi.org\/10.1021\/acs.jcim.8b00035","journal-title":"J Chem Inf Model"},{"key":"1015_CR6","doi-asserted-by":"publisher","first-page":"846","DOI":"10.3390\/pharmaceutics12090846","volume":"12","author":"M Deodhar","year":"2020","unstructured":"Deodhar M, Al Rihani SB, Arwood MJ, Darakjian L, Dow P, Turgeon J, Michaud V (2020) Mechanisms of CYP450 inhibition: understanding drug-drug interactions due to mechanism-based inhibition in clinical practice. Pharmaceutics 12:846. https:\/\/doi.org\/10.3390\/pharmaceutics12090846","journal-title":"Pharmaceutics"},{"key":"1015_CR7","doi-asserted-by":"publisher","first-page":"4336","DOI":"10.1021\/acs.molpharmaceut.8b00110","volume":"15","author":"X Li","year":"2018","unstructured":"Li X, Xu Y, Lai L, Pei J (2018) Prediction of human cytochrome P450 inhibition using a multitask deep autoencoder neural network. Mol Pharm 15:4336\u20134345. https:\/\/doi.org\/10.1021\/acs.molpharmaceut.8b00110","journal-title":"Mol Pharm"},{"key":"1015_CR8","doi-asserted-by":"publisher","first-page":"1099093","DOI":"10.3389\/fphar.2023.1099093","volume":"14","author":"D Ai","year":"2023","unstructured":"Ai D, Cai H, Wei J, Zhao D, Chen Y, Wang L (2023) DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction. Front Pharmacol 14:1099093. https:\/\/doi.org\/10.3389\/fphar.2023.1099093","journal-title":"Front Pharmacol"},{"key":"1015_CR9","doi-asserted-by":"publisher","first-page":"W580","DOI":"10.1093\/nar\/gkaa166","volume":"48","author":"P Banerjee","year":"2020","unstructured":"Banerjee P, Dunkel M, Kemmler E, Preissner R (2020) SuperCYPsPred\u2014a web server for the prediction of cytochrome activity. Nucleic Acids Res 48:W580\u2013W585. https:\/\/doi.org\/10.1093\/nar\/gkaa166","journal-title":"Nucleic Acids Res"},{"key":"1015_CR10","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1021\/ci200028n","volume":"51","author":"F Cheng","year":"2011","unstructured":"Cheng F, Yu Y, Shen J, Yang L, Li W, Liu G, Lee PW, Tang Y (2011) Classification of cytochrome P450 inhibitors and noninhibitors using combined classifiers. J Chem Inf Model 51:996\u20131011. https:\/\/doi.org\/10.1021\/ci200028n","journal-title":"J Chem Inf Model"},{"key":"1015_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.bmc.2021.116388","volume":"46","author":"W Plonka","year":"2021","unstructured":"Plonka W, Stork C, \u0160\u00edcho M, Kirchmair J (2021) CYPlebrity: machine learning models for the prediction of inhibitors of cytochrome P450 enzymes. Bioorg Med Chem 46:116388. https:\/\/doi.org\/10.1016\/j.bmc.2021.116388","journal-title":"Bioorg Med Chem"},{"key":"1015_CR12","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1002\/minf.201100052","volume":"31","author":"T Zhang","year":"2012","unstructured":"Zhang T, Dai H, Liu LA, Lewis DFV, Wei D (2012) Classification models for predicting cytochrome P450 enzyme-substrate selectivity. Mol Inform 31:53\u201362. https:\/\/doi.org\/10.1002\/minf.201100052","journal-title":"Mol Inform"},{"key":"1015_CR13","doi-asserted-by":"publisher","first-page":"2474","DOI":"10.1021\/ci200311w","volume":"51","author":"H Sun","year":"2011","unstructured":"Sun H, Veith H, Xia M, Austin CP, Huang R (2011) Predictive models for cytochrome P450 isozymes based on quantitative high throughput screening data. J Chem Inf Model 51:2474\u20132481. https:\/\/doi.org\/10.1021\/ci200311w","journal-title":"J Chem Inf Model"},{"key":"1015_CR14","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2015.00123","author":"H Raunio","year":"2015","unstructured":"Raunio H, Kuusisto M, Juvonen RO, Pentik\u00e4inen OT (2015) Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes. Front Pharmacol. https:\/\/doi.org\/10.3389\/fphar.2015.00123","journal-title":"Front Pharmacol"},{"key":"1015_CR15","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1124\/pr.115.011411","volume":"68","author":"JT Backman","year":"2016","unstructured":"Backman JT, Filppula AM, Niemi M, Neuvonen PJ (2016) Role of cytochrome P450 2C8 in drug metabolism and interactions. Pharmacol Rev 68:168\u2013241. https:\/\/doi.org\/10.1124\/pr.115.011411","journal-title":"Pharmacol Rev"},{"key":"1015_CR16","unstructured":"U.S. Food and Drug Administration (2012) Guidance for industry: drug interaction studies - study design, data analysis, implications for dosing, and labeling recommendations"},{"key":"1015_CR17","unstructured":"U.S. Food and Drug Administration (2006) Guidance for industry: drug interaction studies - study design, data analysis, and implications for dosing and labeling"},{"key":"1015_CR18","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1038\/s41524-023-01000-z","volume":"9","author":"P Xu","year":"2023","unstructured":"Xu P, Ji X, Li M, Lu W (2023) Small data machine learning in materials science. NPJ Comput Mater 9:42. https:\/\/doi.org\/10.1038\/s41524-023-01000-z","journal-title":"NPJ Comput Mater"},{"key":"1015_CR19","doi-asserted-by":"publisher","first-page":"D930","DOI":"10.1093\/nar\/gky1075","volume":"47","author":"D Mendez","year":"2019","unstructured":"Mendez D, Gaulton A, Bento AP, Chambers J, De Veij M, F\u00e9lix E, Magari\u00f1os MP, Mosquera JF, Mutowo P, Nowotka M, Gordillo-Mara\u00f1\u00f3n M, Hunter F, Junco L, Mugumbate G, Rodriguez-Lopez M, Atkinson F, Bosc N, Radoux CJ, Segura-Cabrera A, Hersey A, Leach AR (2019) ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res 47:D930\u2013D940. https:\/\/doi.org\/10.1093\/nar\/gky1075","journal-title":"Nucleic Acids Res"},{"key":"1015_CR20","doi-asserted-by":"publisher","first-page":"D1373","DOI":"10.1093\/nar\/gkac956","volume":"51","author":"S Kim","year":"2023","unstructured":"Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, Zaslavsky L, Zhang J, Bolton EE (2023) PubChem 2023 update. Nucleic Acids Res 51:D1373\u2013D1380. https:\/\/doi.org\/10.1093\/nar\/gkac956","journal-title":"Nucleic Acids Res"},{"key":"1015_CR21","doi-asserted-by":"publisher","first-page":"5875","DOI":"10.3390\/molecules27185875","volume":"27","author":"A Rudik","year":"2022","unstructured":"Rudik A, Dmitriev A, Lagunin A, Filimonov D, Poroikov V (2022) Computational prediction of inhibitors and inducers of the major isoforms of cytochrome P450. Molecules 27:5875. https:\/\/doi.org\/10.3390\/molecules27185875","journal-title":"Molecules"},{"key":"1015_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.comtox.2019.100089","volume":"12","author":"LSK Konda","year":"2019","unstructured":"Konda LSK, Keerthi Praba S, Kristam R (2019) hERG liability classification models using machine learning techniques. Comput Toxicol 12:100089. https:\/\/doi.org\/10.1016\/j.comtox.2019.100089","journal-title":"Comput Toxicol"},{"key":"1015_CR23","doi-asserted-by":"publisher","first-page":"1850","DOI":"10.1021\/acs.chemrestox.1c00078","volume":"34","author":"X Zhang","year":"2021","unstructured":"Zhang X, Zhao P, Wang Z, Xu X, Liu G, Tang Y, Li W (2021) In silico prediction of CYP2C8 inhibition with machine-learning methods. Chem Res Toxicol 34:1850\u20131859. https:\/\/doi.org\/10.1021\/acs.chemrestox.1c00078","journal-title":"Chem Res Toxicol"},{"key":"1015_CR24","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1009820","volume":"18","author":"E Goldwaser","year":"2022","unstructured":"Goldwaser E, Laurent C, Lagarde N, Fabrega S, Nay L, Villoutreix BO, Jelsch C, Nicot AB, Loriot M-A, Miteva MA (2022) Machine learning-driven identification of drugs inhibiting cytochrome P450 2C9. PLOS Comput Biol 18:e1009820. https:\/\/doi.org\/10.1371\/journal.pcbi.1009820","journal-title":"PLOS Comput Biol"},{"issue":"29","key":"1015_CR25","doi-asserted-by":"publisher","first-page":"861","DOI":"10.21105\/joss.00861","volume":"3","author":"L McInnes","year":"2018","unstructured":"McInnes L, Healy J, Saul N, Gro\u00dfberger L (2018) UMAP: uniform manifold approximation and projection. J Open Source Softw 3(29):861. https:\/\/doi.org\/10.21105\/joss.00861","journal-title":"J Open Source Softw"},{"key":"1015_CR26","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/s13321-020-00435-6","volume":"12","author":"R Kojima","year":"2020","unstructured":"Kojima R, Ishida S, Ohta M, Iwata H, Honma T, Okuno Y (2020) kGCN: a graph-based deep learning framework for chemical structures. J Cheminform 12:32. https:\/\/doi.org\/10.1186\/s13321-020-00435-6","journal-title":"J Cheminform"},{"key":"1015_CR27","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.7542","volume":"35","author":"C \u00d6zt\u00fcrk","year":"2023","unstructured":"\u00d6zt\u00fcrk C, Ta\u015fy\u00fcrek M, T\u00fcrkdamar MU (2023) Transfer learning and fine-tuned transfer learning methods\u2019 effectiveness analyse in the CNN-based deep learning models. Concurr Comput Pract Exp 35:e7542. https:\/\/doi.org\/10.1002\/cpe.7542","journal-title":"Concurr Comput Pract Exp"},{"key":"1015_CR28","doi-asserted-by":"publisher","first-page":"1145209","DOI":"10.3389\/fncom.2023.1145209","volume":"17","author":"Z He","year":"2023","unstructured":"He Z, Zhang L, Wang H (2023) An initial prediction and fine-tuning model based on improving GCN for 3D human motion prediction. Front Comput Neurosci 17:1145209. https:\/\/doi.org\/10.3389\/fncom.2023.1145209","journal-title":"Front Comput Neurosci"},{"key":"1015_CR29","doi-asserted-by":"publisher","first-page":"5437","DOI":"10.1007\/s11033-022-07231-5","volume":"49","author":"A Abbas","year":"2022","unstructured":"Abbas A, Shah AN, Tanveer M, Ahmed W, Shah AA, Fiaz S, Waqas MM, Ullah S (2022) MiRNA fine tuning for crop improvement: using advance computational models and biotechnological tools. Mol Biol Rep 49:5437\u20135450. https:\/\/doi.org\/10.1007\/s11033-022-07231-5","journal-title":"Mol Biol Rep"},{"key":"1015_CR30","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1016\/j.gltp.2021.08.003","volume":"2","author":"MK Bohmrah","year":"2021","unstructured":"Bohmrah MK, Kaur H (2021) Classification of Covid-19 patients using efficient fine-tuned deep learning DenseNet model. Glob Transit Proc 2:476\u2013483. https:\/\/doi.org\/10.1016\/j.gltp.2021.08.003","journal-title":"Glob Transit Proc"},{"key":"1015_CR31","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1186\/s12955-019-1181-2","volume":"17","author":"OF Ayilara","year":"2019","unstructured":"Ayilara OF, Zhang L, Sajobi TT, Sawatzky R, Bohm E, Lix LM (2019) Impact of missing data on bias and precision when estimating change in patient-reported outcomes from a clinical registry. Health Qual Life Outcomes 17:106. https:\/\/doi.org\/10.1186\/s12955-019-1181-2","journal-title":"Health Qual Life Outcomes"},{"key":"1015_CR32","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1186\/s40537-021-00516-9","volume":"8","author":"T Emmanuel","year":"2021","unstructured":"Emmanuel T, Maupong T, Mpoeleng D, Semong T, Mphago B, Tabona O (2021) A survey on missing data in machine learning. J Big Data 8:140. https:\/\/doi.org\/10.1186\/s40537-021-00516-9","journal-title":"J Big Data"},{"key":"1015_CR33","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1016\/j.jclinepi.2006.01.014","volume":"59","author":"ART Donders","year":"2006","unstructured":"Donders ART, Van Der Heijden GJMG, Stijnen T, Moons KGM (2006) Review: a gentle introduction to imputation of missing values. J Clin Epidemiol 59:1087\u20131091. https:\/\/doi.org\/10.1016\/j.jclinepi.2006.01.014","journal-title":"J Clin Epidemiol"},{"key":"1015_CR34","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1038\/s43856-023-00356-z","volume":"3","author":"T Shadbahr","year":"2023","unstructured":"Shadbahr T, Roberts M, Stanczuk J, Gilbey J, Teare P, Dittmer S, Thorpe M, Torn\u00e9 RV, Sala E, Li\u00f3 P, Patel M, Preller J, Collaboration AIX-COVNET, Selby I, Breger A, Weir-McCall JR, Gkrania-Klotsas E, Korhonen A, Jefferson E, Langs G, Yang G, Prosch H, Babar J, Escudero S\u00e1nchez L, Wassin M, Holzer M, Walton N, Li\u00f3 P, Rudd JHF, Mirtti T, Rannikko AS, Aston JAD, Tang J, Sch\u00f6nlieb C-B (2023) The impact of imputation quality on machine learning classifiers for datasets with missing values. Commun Med 3:139. https:\/\/doi.org\/10.1038\/s43856-023-00356-z","journal-title":"Commun Med"},{"key":"1015_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100799","volume":"27","author":"MdK Hasan","year":"2021","unstructured":"Hasan MdK, Alam MdA, Roy S, Dutta A, Jawad MdT, Das S (2021) Missing value imputation affects the performance of machine learning: a review and analysis of the literature (2010\u20132021). Inform Med Unlocked 27:100799. https:\/\/doi.org\/10.1016\/j.imu.2021.100799","journal-title":"Inform Med Unlocked"},{"key":"1015_CR36","doi-asserted-by":"publisher","first-page":"76516","DOI":"10.1109\/ACCESS.2020.2989857","volume":"8","author":"MdK Hasan","year":"2020","unstructured":"Hasan MdK, Alam MdA, Das D, Hossain E, Hasan M (2020) Diabetes prediction using ensembling of different machine learning classifiers. IEEE Access 8:76516\u201376531. https:\/\/doi.org\/10.1109\/ACCESS.2020.2989857","journal-title":"IEEE Access"},{"key":"1015_CR37","doi-asserted-by":"publisher","first-page":"D1265","DOI":"10.1093\/nar\/gkad976","volume":"52","author":"C Knox","year":"2024","unstructured":"Knox C, Wilson M, Klinger CM, Franklin M, Oler E, Wilson A, Pon A, Cox J, Chin NE, Strawbridge SA, Garcia-Patino M, Kruger R, Sivakumaran A, Sanford S, Doshi R, Khetarpal N, Fatokun O, Doucet D, Zubkowski A, Rayat DY, Jackson H, Harford K, Anjum A, Zakir M, Wang F, Tian S, Lee B, Liigand J, Peters H, Wang RQ, Nguyen T, So D, Sharp M, da Silva R, Gabriel C, Scantlebury J, Jasinski M, Ackerman D, Jewison T, Sajed T, Gautam V, Wishart DS (2024) DrugBank 6.0: the DrugBank Knowledgebase for 2024. Nucleic Acids Res 52:D1265\u2013D1275. https:\/\/doi.org\/10.1093\/nar\/gkad976","journal-title":"Nucleic Acids Res"},{"key":"1015_CR38","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/s40262-015-0314-y","volume":"55","author":"S Ouwerkerk-Mahadevan","year":"2016","unstructured":"Ouwerkerk-Mahadevan S, Snoeys J, Peeters M, Beumont-Mauviel M, Simion A (2016) Drug-drug interactions with the NS3\/4A protease inhibitor Simeprevir. Clin Pharmacokinet 55:197\u2013208. https:\/\/doi.org\/10.1007\/s40262-015-0314-y","journal-title":"Clin Pharmacokinet"},{"key":"1015_CR39","doi-asserted-by":"publisher","first-page":"73","DOI":"10.56095\/eaj.v3i3.58","volume":"3","author":"N Ferri","year":"2024","unstructured":"Ferri N, Corsini A, Pontremoli R (2024) Antihypertensive and renal protection effects of lercanidipine and lercanidipine\/enalapril: Renal protection by lercanidipine. Eur Atheroscler J 3:73\u201380. https:\/\/doi.org\/10.56095\/eaj.v3i3.58","journal-title":"Eur Atheroscler J"},{"key":"1015_CR40","doi-asserted-by":"crossref","unstructured":"Berthold MR, Cebron N, Dill F, Gabriel TR, K\u00f6tter T, Meinl T, Ohl P, Sieb C, Thiel K, Wiswedel B (2007) KNIME: The Konstanz Information Miner. In: Studies in classification, data analysis, and knowledge organization (GfKL 2007). Springer","DOI":"10.1007\/978-3-540-78246-9_38"},{"key":"1015_CR41","doi-asserted-by":"publisher","first-page":"D1035","DOI":"10.1093\/nar\/gkq1126","volume":"39","author":"C Knox","year":"2011","unstructured":"Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Wishart DS (2011) DrugBank 3.0: a comprehensive resource for \u201cOmics\u201d research on drugs. Nucleic Acids Res 39:D1035\u2013D1041. https:\/\/doi.org\/10.1093\/nar\/gkq1126","journal-title":"Nucleic Acids Res"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-01015-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-025-01015-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-01015-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T08:19:21Z","timestamp":1746519561000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-025-01015-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,30]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1015"],"URL":"https:\/\/doi.org\/10.1186\/s13321-025-01015-2","relation":{},"ISSN":["1758-2946"],"issn-type":[{"type":"electronic","value":"1758-2946"}],"subject":[],"published":{"date-parts":[[2025,4,30]]},"assertion":[{"value":"7 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"66"}}