{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T07:12:57Z","timestamp":1773817977229,"version":"3.50.1"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T00:00:00Z","timestamp":1744243200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T00:00:00Z","timestamp":1744243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003569","name":"Ministry of Food and Drug Safety","doi-asserted-by":"publisher","award":["RS-2024-00331849"],"award-info":[{"award-number":["RS-2024-00331849"]}],"id":[{"id":"10.13039\/501100003569","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00399364"],"award-info":[{"award-number":["RS-2024-00399364"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>The development of robust artificial intelligence (AI)-driven predictive models relies on high-quality, diverse chemical datasets. However, the scarcity of negative data and a publication bias toward positive results often hinder accurate biological activity prediction. To address this challenge, we introduce InertDB, a comprehensive database comprising 3,205 curated inactive compounds (CICs) identified through rigorous review of over 4.6 million compound records in PubChem. CIC selection prioritized bioassay diversity, determined using natural language processing (NLP)-based clustering metrics, while ensuring minimal biological activity across all evaluated bioassays. Notably, 97.2% of CICs adhere to the Rule of Five, a proportion significantly higher than that of overall PubChem dataset. To further expand the chemical space, InertDB also features 64,368 generated inactive compounds (GICs) produced using a deep generative AI model trained on the CIC dataset. Compared to conventional approaches such as random sampling or property-matched decoys, InertDB significantly improves predictive AI performance, particularly for phenotypic activity prediction by providing reliable inactive compound sets.<\/jats:p>\n          <jats:p>\n            <jats:bold>Scientific contributions<\/jats:bold>\n          <\/jats:p>\n          <jats:p>InertDB addresses a critical gap in AI-driven drug discovery by providing a comprehensive repository of biologically inactive compounds, effectively resolving the scarcity of negative data that limits prediction accuracy and model reliability. By leveraging language model-based bioassay diversity metrics and generative AI, InertDB integrates rigorously curated inactive compounds with an expanded chemical space. InertDB serves as a valuable alternative to random sampling and decoy generation, offering improved training datasets and enhancing the accuracy of phenotypic pharmacological activity prediction.<\/jats:p>","DOI":"10.1186\/s13321-025-00999-1","type":"journal-article","created":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T10:13:31Z","timestamp":1744280011000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["InertDB as a generative AI-expanded resource of biologically inactive small molecules from PubChem"],"prefix":"10.1186","volume":"17","author":[{"given":"Seungchan","family":"An","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yeonjin","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junpyo","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seokyoung","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"In Guk","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jayhyun","family":"Cho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min Ju","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minkyu","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun Pyo","family":"Kang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minsoo","family":"Noh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,10]]},"reference":[{"key":"999_CR1","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1038\/s41573-019-0050-3","volume":"19","author":"P Schneider","year":"2020","unstructured":"Schneider P, Walters WP, Plowright AT, Sieroka N, Listgarten J, Goodnow RA, Fisher J, Jansen JM, Duca JS, Rush TS, Zentgraf M, Hill JE, Krutoholow E, Kohler M, Blaney J, Funatsu K, Luebkemann C, Schneider G (2020) Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discov 19:353\u2013364","journal-title":"Nat Rev Drug Discov"},{"key":"999_CR2","doi-asserted-by":"publisher","first-page":"10520","DOI":"10.1021\/acs.chemrev.8b00728","volume":"119","author":"X Yang","year":"2019","unstructured":"Yang X, Wang Y, Byrne R, Schneider G, Yang S (2019) Concepts of artificial intelligence for computer-assisted drug discovery. Chem Rev 119:10520\u201310594","journal-title":"Chem Rev"},{"key":"999_CR3","doi-asserted-by":"publisher","first-page":"5386","DOI":"10.1021\/acs.jcim.1c00733","volume":"61","author":"Y Hao","year":"2021","unstructured":"Hao Y, Moore JH (2021) TargetTox: a feature selection pipeline for identifying predictive targets associated with drug toxicity. J Chem Inf Model 61:5386\u20135394","journal-title":"J Chem Inf Model"},{"key":"999_CR4","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab430","author":"J Deng","year":"2022","unstructured":"Deng J, Yang Z, Ojima I, Samaras D, Wang F (2022) Artificial intelligence in drug discovery: applications and techniques. Brief Bioinform. https:\/\/doi.org\/10.1093\/bib\/bbab430","journal-title":"Brief Bioinform"},{"key":"999_CR5","doi-asserted-by":"publisher","first-page":"2628","DOI":"10.1021\/acs.jcim.3c00200","volume":"63","author":"TTV Tran","year":"2023","unstructured":"Tran TTV, Surya Wibowo A, Tayara H, Chong KT (2023) Artificial intelligence in drug toxicity prediction: recent advances, challenges, and future perspectives. J Chem Inf Model 63:2628\u20132643","journal-title":"J Chem Inf Model"},{"key":"999_CR6","doi-asserted-by":"publisher","first-page":"1938","DOI":"10.1039\/D3SC05534E","volume":"15","author":"K Martinez-Mayorga","year":"2024","unstructured":"Martinez-Mayorga K, Rosas-Jim\u00e9nez JG, Gonzalez-Ponce K, L\u00f3pez-L\u00f3pez E, Neme A, Medina-Franco JL (2024) The pursuit of accurate predictive models of the bioactivity of small molecules. Chem Sci 15:1938\u20131952","journal-title":"Chem Sci"},{"key":"999_CR7","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1016\/j.omtn.2023.02.019","volume":"31","author":"W Chen","year":"2023","unstructured":"Chen W, Liu X, Zhang S, Chen S (2023) Artificial intelligence for drug discovery: Resources, methods, and applications. Mol Ther Nucleic Acids 31:691\u2013702","journal-title":"Mol Ther Nucleic Acids"},{"key":"999_CR8","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","journal-title":"Nucleic Acids Res"},{"key":"999_CR9","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, de Veij M, Ioannidis H, Lopez DM, Mosquera JF, Magarinos MP, Bosc N, Arcila R, Kizil\u00f6ren T, Gaulton A, Bento AP, Adasme MF, Monecke P, Landrum GA, Leach AR (2024) The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Res 52:D1180\u2013D1192","journal-title":"Nucleic Acids Res"},{"key":"999_CR10","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/s13321-016-0142-6","volume":"8","author":"S Kim","year":"2016","unstructured":"Kim S, Thiessen PA, Cheng T, Yu B, Shoemaker BA, Wang J, Bolton EE, Wang Y, Bryant SH (2016) Literature information in PubChem: associations between PubChem records and scientific articles. J Cheminform 8:32","journal-title":"J Cheminform"},{"key":"999_CR11","doi-asserted-by":"publisher","first-page":"2353","DOI":"10.1016\/j.drudis.2022.05.005","volume":"27","author":"E L\u00f3pez-L\u00f3pez","year":"2022","unstructured":"L\u00f3pez-L\u00f3pez E, Fern\u00e1ndez-de Gortari E, Medina-Franco JL (2022) Yes SIR! On the structure-inactivity relationships in drug discovery. Drug Discov Today 27:2353\u20132362","journal-title":"Drug Discov Today"},{"key":"999_CR12","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1038\/s43588-024-00699-0","volume":"4","author":"G Durant","year":"2024","unstructured":"Durant G, Boyles F, Birchall K, Deane CM (2024) The future of machine learning for small-molecule drug discovery will be driven by data. Nat Comput Sci 4:735\u2013743","journal-title":"Nat Comput Sci"},{"key":"999_CR13","doi-asserted-by":"publisher","first-page":"6065","DOI":"10.1021\/acs.jcim.0c00675","volume":"60","author":"JJ Irwin","year":"2020","unstructured":"Irwin JJ, Tang KG, Young J, Dandarchuluun C, Wong BR, Khurelbaatar M, Moroz YS, Mayfield J, Sayle RA (2020) ZINC20-A Free Ultralarge-Scale Chemical Database for Ligand Discovery. J Chem Inf Model 60:6065\u20136073","journal-title":"J Chem Inf Model"},{"key":"999_CR14","doi-asserted-by":"publisher","first-page":"1595","DOI":"10.1021\/ci4002712","volume":"53","author":"K Heikamp","year":"2013","unstructured":"Heikamp K, Bajorath J (2013) Comparison of confirmed inactive and randomly selected compounds as negative training examples in support vector machine-based virtual screening. J Chem Inf Model 53:1595\u20131601","journal-title":"J Chem Inf Model"},{"key":"999_CR15","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1021\/ci800022e","volume":"48","author":"XH Ma","year":"2008","unstructured":"Ma XH, Wang R, Yang SY, Li ZR, Xue Y, Wei YC, Low BC, Chen YZ (2008) Evaluation of virtual screening performance of support vector machines trained by sparsely distributed active compounds. J Chem Inf Model 48:1227\u20131237","journal-title":"J Chem Inf Model"},{"key":"999_CR16","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1021\/ci900382e","volume":"50","author":"T Sato","year":"2010","unstructured":"Sato T, Honma T, Yokoyama S (2010) Combining machine learning and pharmacophore-based interaction fingerprint for in silico screening. J Chem Inf Model 50:170\u2013185","journal-title":"J Chem Inf Model"},{"key":"999_CR17","doi-asserted-by":"publisher","first-page":"1364","DOI":"10.1021\/ci100464b","volume":"51","author":"JX Ren","year":"2011","unstructured":"Ren JX, Li LL, Zheng RL, Xie HZ, Cao ZX, Feng S, Pan YL, Chen X, Wei YQ, Yang SY (2011) Discovery of novel Pim-1 kinase inhibitors by a hierarchical multistage virtual screening approach based on SVM model, pharmacophore, and molecular docking. J Chem Inf Model 51:1364\u20131375","journal-title":"J Chem Inf Model"},{"key":"999_CR18","doi-asserted-by":"publisher","first-page":"1827","DOI":"10.1016\/j.bmcl.2015.03.049","volume":"25","author":"S Smusz","year":"2015","unstructured":"Smusz S, Kurczab R, Sata\u0142a G, Bojarski AJ (2015) Fingerprint-based consensus virtual screening towards structurally new 5-HT(6)R ligands. Bioorg Med Chem Lett 25:1827\u20131830","journal-title":"Bioorg Med Chem Lett"},{"key":"999_CR19","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab135","author":"Y Zhao","year":"2021","unstructured":"Zhao Y, Wang XG, Ma ZY, Xiong GL, Yang ZJ, Cheng Y, Lu AP, Huang ZJ, Cao DS (2021) Systematic comparison of ligand-based and structure-based virtual screening methods on poly (ADP-ribose) polymerase-1 inhibitors. Brief Bioinform. https:\/\/doi.org\/10.1093\/bib\/bbab135","journal-title":"Brief Bioinform"},{"key":"999_CR20","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1021\/acs.jcim.3c00218","volume":"63","author":"VK Tran-Nguyen","year":"2023","unstructured":"Tran-Nguyen VK, Ballester PJ (2023) Beware of simple methods for structure-based virtual screening: the critical importance of broader comparisons. J Chem Inf Model 63:1401\u20131405","journal-title":"J Chem Inf Model"},{"key":"999_CR21","doi-asserted-by":"publisher","first-page":"6582","DOI":"10.1021\/jm300687e","volume":"55","author":"MM Mysinger","year":"2012","unstructured":"Mysinger MM, Carchia M, Irwin JJ, Shoichet BK (2012) Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J Med Chem 55:6582\u20136594","journal-title":"J Med Chem"},{"key":"999_CR22","doi-asserted-by":"crossref","unstructured":"Ibrahim TM, Bauer MR, Boeckler FM (2015) Applying DEKOIS 2.0 in structure-based virtual screening to probe the impact of preparation procedures and score normalization. J Cheminform. 7: 21.","DOI":"10.1186\/s13321-015-0074-6"},{"key":"999_CR23","doi-asserted-by":"publisher","first-page":"2134","DOI":"10.1093\/bioinformatics\/btab080","volume":"37","author":"F Imrie","year":"2021","unstructured":"Imrie F, Bradley AR, Deane CM (2021) Generating property-matched decoy molecules using deep learning. Bioinformatics 37:2134\u20132141","journal-title":"Bioinformatics"},{"key":"999_CR24","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3389\/fphar.2018.00011","volume":"9","author":"M R\u00e9au","year":"2018","unstructured":"R\u00e9au M, Langenfeld F, Zagury JF, Lagarde N, Montes M (2018) Decoys selection in benchmarking datasets: overview and perspectives. Front Pharmacol 9:11","journal-title":"Front Pharmacol"},{"key":"999_CR25","doi-asserted-by":"publisher","first-page":"3690","DOI":"10.1021\/acschemneuro.1c00430","volume":"12","author":"AK Dhanabalan","year":"2021","unstructured":"Dhanabalan AK, Subaraja M, Palanichamy K, Velmurugan D, Gunasekaran K (2021) Identification of a Chlorogenic Ester as a Monoamine Oxidase (MAO-B) Inhibitor by Integrating \u201cTraditional and Machine Learning\u201d Virtual Screening and In Vitro as well as In Vivo Validation: A Lead against Neurodegenerative Disorders? ACS Chem Neurosci 12:3690\u20133707","journal-title":"ACS Chem Neurosci"},{"key":"999_CR26","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1186\/s13321-024-00832-1","volume":"16","author":"K Caba","year":"2024","unstructured":"Caba K, Tran-Nguyen VK, Rahman T, Ballester PJ (2024) Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors. J Cheminform 16:40","journal-title":"J Cheminform"},{"key":"999_CR27","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.jare.2024.01.024","volume":"67","author":"P G\u00f3mez-Sacrist\u00e1n","year":"2025","unstructured":"G\u00f3mez-Sacrist\u00e1n P, Simeon S, Tran-Nguyen VK, Patil S, Ballester PJ (2025) Inactive-enriched machine-learning models exploiting patent data improve structure-based virtual screening for PDL1 dimerizers. J Adv Res 67:185\u2013196","journal-title":"J Adv Res"},{"key":"999_CR28","doi-asserted-by":"crossref","unstructured":"Ren Q, Qu N, Sun J, Zhou J, Liu J, Ni L, Tong X, Zhang Z, Kong X, Wen Y, Wang Y, Wang D, Luo X, Zhang S, Zheng M, Li X. 2023. KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling. Brief Bioinform. 25:bbad461.","DOI":"10.1093\/bib\/bbad461"},{"key":"999_CR29","doi-asserted-by":"publisher","first-page":"D400","DOI":"10.1093\/nar\/gkr1132","volume":"40","author":"Y Wang","year":"2012","unstructured":"Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Zhou Z, Han L, Karapetyan K, Dracheva S, Shoemaker BA, Bolton E, Gindulyte A, Bryant SH (2012) PubChem\u2019s BioAssay Database. Nucleic Acids Res 40:D400\u2013D412","journal-title":"Nucleic Acids Res"},{"key":"999_CR30","doi-asserted-by":"publisher","first-page":"856","DOI":"10.1021\/acs.jcim.3c00033","volume":"63","author":"S An","year":"2023","unstructured":"An S, Hwang SY, Gong J, Ahn S, Park IG, Oh S, Chin YW, Noh M (2023) Computational Prediction of the Phenotypic Effect of Flavonoids on Adiponectin Biosynthesis. J Chem Inf Model 63:856\u2013869","journal-title":"J Chem Inf Model"},{"key":"999_CR31","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1038\/nrc1951","volume":"6","author":"RH Shoemaker","year":"2006","unstructured":"Shoemaker RH (2006) The NCI60 human tumour cell line anticancer drug screen. Nat Rev Cancer 6:813\u2013823","journal-title":"Nat Rev Cancer"},{"key":"999_CR32","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1021\/ci000321u","volume":"40","author":"JW Godden","year":"2000","unstructured":"Godden JW, Stahura FL, Bajorath J (2000) Variability of molecular descriptors in compound databases revealed by Shannon entropy calculations. J Chem Inf Comput Sci 40:796\u2013800","journal-title":"J Chem Inf Comput Sci"},{"key":"999_CR33","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1021\/ci600377m","volume":"47","author":"J Batista","year":"2007","unstructured":"Batista J, Bajorath J (2007) Chemical database mining through entropy-based molecular similarity assessment of randomly generated structural fragment populations. J Chem Inf Model 47:59\u201368","journal-title":"J Chem Inf Model"},{"key":"999_CR34","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.ddtec.2018.03.001","volume":"27","author":"G Caron","year":"2018","unstructured":"Caron G, Vallaro M, Ermondi G (2018) Log P as a tool in intramolecular hydrogen bond considerations. Drug Discov Today Technol 27:65\u201370","journal-title":"Drug Discov Today Technol"},{"key":"999_CR35","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.1501240","volume":"2","author":"D Chen","year":"2016","unstructured":"Chen D, Oezguen N, Urvil P, Ferguson C, Dann SM, Savidge TC (2016) Regulation of protein-ligand binding affinity by hydrogen bond pairing. Sci Adv 2:e1501240","journal-title":"Sci Adv"},{"key":"999_CR36","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.1021\/jm020017n","volume":"45","author":"DF Veber","year":"2002","unstructured":"Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45:2615\u20132623","journal-title":"J Med Chem"},{"key":"999_CR37","doi-asserted-by":"publisher","first-page":"2636","DOI":"10.1021\/acs.jmedchem.7b00717","volume":"61","author":"DA DeGoey","year":"2018","unstructured":"DeGoey DA, Chen HJ, Cox PB, Wendt MD (2018) Beyond the Rule of 5: Lessons Learned from AbbVie\u2019s Drugs and Compound Collection. J Med Chem 61:2636\u20132651","journal-title":"J Med Chem"},{"key":"999_CR38","doi-asserted-by":"publisher","first-page":"2719","DOI":"10.1021\/jm901137j","volume":"53","author":"JB Baell","year":"2010","unstructured":"Baell JB, Holloway GA (2010) New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem 53:2719\u20132740","journal-title":"J Med Chem"},{"key":"999_CR39","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1021\/acschembio.7b00903","volume":"13","author":"JB Baell","year":"2018","unstructured":"Baell JB, Nissink JWM (2018) Seven Year Itch: Pan-Assay Interference Compounds (PAINS) in 2017-Utility and Limitations. ACS Chem Biol 13:36\u201344","journal-title":"ACS Chem Biol"},{"key":"999_CR40","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1021\/acscentsci.7b00512","volume":"4","author":"MHS Segler","year":"2018","unstructured":"Segler MHS, Kogej T, Tyrchan C, Waller MP (2018) Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. ACS Cent Sci 4:120\u2013131","journal-title":"ACS Cent Sci"},{"key":"999_CR41","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1038\/s42256-021-00407-x","volume":"3","author":"MA Skinnider","year":"2021","unstructured":"Skinnider MA, Wang F, Pasin D, Greiner R, Foster LJ, Dalsgaard PW, Wishart DS (2021) A deep generative model enables automated structure elucidation of novel psychoactive substances. Nat Mach Intell 3:973\u2013984","journal-title":"Nat Mach Intell"},{"key":"999_CR42","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1038\/s42256-021-00368-1","volume":"3","author":"MA Skinnider","year":"2021","unstructured":"Skinnider MA, Stacey RG, Wishart DS, Foster LJ (2021) Chemical language models enable navigation in sparsely populated chemical space. Nat Mach Intell 3:759\u2013770","journal-title":"Nat Mach Intell"},{"key":"999_CR43","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1186\/s13321-019-0393-0","volume":"11","author":"J Ar\u00fas-Pous","year":"2019","unstructured":"Ar\u00fas-Pous J, Johansson SV, Prykhodko O, Bjerrum EJ, Tyrchan C, Reymond JL, Chen H, Engkvist O (2019) Randomized SMILES strings improve the quality of molecular generative models. J Cheminform 11:71","journal-title":"J Cheminform"},{"key":"999_CR44","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1038\/nchembio.1936","volume":"11","author":"AM Wassermann","year":"2015","unstructured":"Wassermann AM, Lounkine E, Hoepfner D, Le Goff G, King FJ, Studer C, Peltier JM, Grippo ML, Prindle V, Tao J, Schuffenhauer A, Wallace IM, Chen S, Krastel P, Cobos-Correa A, Parker CN, Davies JW, Glick M (2015) Dark chemical matter as a promising starting point for drug lead discovery. Nat Chem Biol 11:958\u2013966","journal-title":"Nat Chem Biol"},{"key":"999_CR45","doi-asserted-by":"publisher","first-page":"4263","DOI":"10.1021\/acs.jcim.0c00155","volume":"60","author":"VK Tran-Nguyen","year":"2020","unstructured":"Tran-Nguyen VK, Jacquemard C, Rognan D (2020) LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening. J Chem Inf Model 60:4263\u20134273","journal-title":"J Chem Inf Model"},{"key":"999_CR46","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1021\/ci8002649","volume":"49","author":"SG Rohrer","year":"2009","unstructured":"Rohrer SG, Baumann K (2009) Maximum unbiased validation (MUV) data sets for virtual screening based on PubChem bioactivity data. J Chem Inf Model 49:169\u2013184","journal-title":"J Chem Inf Model"},{"key":"999_CR47","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.1021\/acs.jcim.2c00224","volume":"62","author":"WA Warr","year":"2022","unstructured":"Warr WA, Nicklaus MC, Nicolaou CA, Rarey M (2022) Exploration of Ultralarge Compound Collections for Drug Discovery. J Chem Inf Model 62:2021\u20132034","journal-title":"J Chem Inf Model"},{"key":"999_CR48","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1038\/s41589-022-01234-w","volume":"19","author":"J Lyu","year":"2023","unstructured":"Lyu J, Irwin JJ, Shoichet BK (2023) Modeling the expansion of virtual screening libraries. Nat Chem Biol 19:712\u2013718","journal-title":"Nat Chem Biol"},{"key":"999_CR49","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1038\/s41570-024-00593-3","volume":"8","author":"L Tan","year":"2024","unstructured":"Tan L, Hirte S, Palmacci V, Stork C, Kirchmair J (2024) Tackling assay interference associated with small molecules. Nat Rev Chem 8:319\u2013339","journal-title":"Nat Rev Chem"},{"key":"999_CR50","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s13321-022-00590-y","volume":"14","author":"A Keshavarzi Arshadi","year":"2022","unstructured":"Keshavarzi Arshadi A, Salem M, Firouzbakht A, Yuan JS (2022) MolData, a molecular benchmark for disease and target based machine learning. J Cheminform 14:10","journal-title":"J Cheminform"},{"key":"999_CR51","doi-asserted-by":"crossref","unstructured":"Rohanian O, Nouriborji M, Kouchaki S, Clifton DA (2023) On the effectiveness of compact biomedical transformers. Bioinformatics 39: btad103.","DOI":"10.1093\/bioinformatics\/btad103"},{"key":"999_CR52","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, Kang J (2020) BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36:1234\u20131240","journal-title":"Bioinformatics"},{"key":"999_CR53","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1038\/s41587-020-0502-7","volume":"38","author":"M Duran-Frigola","year":"2020","unstructured":"Duran-Frigola M, Pauls E, Guitart-Pla O, Bertoni M, Alcalde V, Amat D, Juan-Blanco T, Aloy P (2020) Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker. Nat Biotechnol 38:1087\u20131096","journal-title":"Nat Biotechnol"},{"key":"999_CR54","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1802.03426","author":"L McInnes","year":"2018","unstructured":"McInnes L, Healy J, Melville J (2018) UMAP: Uniform Manifold Approximation and Projection. ArXiv. https:\/\/doi.org\/10.48550\/arXiv.1802.03426","journal-title":"ArXiv"},{"key":"999_CR55","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742\u2013754","journal-title":"J Chem Inf Model"},{"key":"999_CR56","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1186\/s13321-016-0174-y","volume":"8","author":"Y Djoumbou Feunang","year":"2016","unstructured":"Djoumbou Feunang Y, Eisner R, Knox C, Chepelev L, Hastings J, Owen G, Fahy E, Steinbeck C, Subramanian S, Bolton E, Greiner R, Wishart DS (2016) ClassyFire: automated chemical classification with a comprehensive, computable taxonomy. J Cheminform 8:61","journal-title":"J Cheminform"},{"key":"999_CR57","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, Kurbanov R, Artamonov A, Aladinskiy V, Veselov M, Kadurin A, Johansson S, Chen H, Nikolenko S, Aspuru-Guzik A, Zhavoronkov A (2020) Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models. Front Pharmacol 11:565644","journal-title":"Front Pharmacol"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-00999-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-025-00999-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-00999-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T12:10:12Z","timestamp":1744287012000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-025-00999-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,10]]},"references-count":57,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["999"],"URL":"https:\/\/doi.org\/10.1186\/s13321-025-00999-1","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,10]]},"assertion":[{"value":"26 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 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":"49"}}