{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:28:22Z","timestamp":1777501702516,"version":"3.51.4"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T00:00:00Z","timestamp":1741564800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T00:00:00Z","timestamp":1741564800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100013060","name":"European Molecular Biology Laboratory","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013060","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>In October 2024 we celebrated the 15th anniversary of the first launch of ChEMBL, Europe\u2019s most impactful, open-access drug discovery database, hosted by EMBL\u2019s European Bioinformatics Institute (EMBL-EBI). This is a good moment to reflect on ChEMBL\u2019s history, the role that ChEMBL plays in Cheminformatics and Drug Discovery as well as innovations accelerated using data extracted from it. The review closes by discussing current challenges and possible directions that need to be taken to guarantee that ChEMBL continues to be the pioneering resource for highly curated, open bioactivity data on the European continent and beyond.<\/jats:p>","DOI":"10.1186\/s13321-025-00963-z","type":"journal-article","created":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T14:33:41Z","timestamp":1741617221000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Fifteen years of ChEMBL and its role in cheminformatics and drug discovery"],"prefix":"10.1186","volume":"17","author":[{"given":"Barbara","family":"Zdrazil","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,10]]},"reference":[{"key":"963_CR1","unstructured":"News article by the Communications Team (2025) The first draft human genome at 20. https:\/\/www.sanger.ac.uk\/news_item\/the-first-draft-human-genome-at-20\/"},{"key":"963_CR2","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s10822-009-9260-9","volume":"23","author":"WA Warr","year":"2009","unstructured":"Warr WA (2009) ChEMBL. An interview with John Overington, team leader, chemogenomics at the European Bioinformatics Institute Outstation of the European Molecular Biology Laboratory (EMBL-EBI). J Comput Aided Mol Des 23:195\u2013198. https:\/\/doi.org\/10.1007\/s10822-009-9260-9","journal-title":"J Comput Aided Mol Des"},{"key":"963_CR3","doi-asserted-by":"publisher","first-page":"1365","DOI":"10.1042\/BST0391365","volume":"39","author":"LJ Bellis","year":"2011","unstructured":"Bellis LJ, Akhtar R, Al-Lazikani B et al (2011) Collation and data-mining of literature bioactivity data for drug discovery. Biochem Soc Trans 39:1365\u20131370. https:\/\/doi.org\/10.1042\/BST0391365","journal-title":"Biochem Soc Trans"},{"key":"963_CR4","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 et al (2012) ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:D1100-1107. https:\/\/doi.org\/10.1093\/nar\/gkr777","journal-title":"Nucleic Acids Res"},{"key":"963_CR5","unstructured":"EMBL-EBI FTP server, ChEMBL_01 https:\/\/ftp.ebi.ac.uk\/pub\/databases\/chembl\/ChEMBLdb\/releases\/chembl_01\/. Accessed 16 Jan 2025"},{"key":"963_CR6","unstructured":"EMBL-EBI FTP server, ChEMBL_03 https:\/\/ftp.ebi.ac.uk\/pub\/databases\/chembl\/ChEMBLdb\/releases\/chembl_03\/chembl_03_release_notes.txt"},{"key":"963_CR7","unstructured":"EMBL-EBI FTP server, ChEMBL_04 https:\/\/ftp.ebi.ac.uk\/pub\/databases\/chembl\/ChEMBLdb\/releases\/chembl_04\/chembl_04_release_notes.txt"},{"key":"963_CR8","unstructured":"ChEMBL-Neglected Tropical Disease archive https:\/\/chembl.gitbook.io\/chembl-ntd. Accessed 16 Jan 2025"},{"key":"963_CR9","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 et al (2023) PubChem 2023 update. Nucleic Acids Res 51:D1373\u2013D1380. https:\/\/doi.org\/10.1093\/nar\/gkac956","journal-title":"Nucleic Acids Res"},{"key":"963_CR10","doi-asserted-by":"publisher","unstructured":"(2011) Guide to Receptors and Channels (GRAC), 5th edition. Br J Pharmacol 164:S1\u2013S2. https:\/\/doi.org\/10.1111\/j.1476-5381.2011.01649_1.x","DOI":"10.1111\/j.1476-5381.2011.01649_1.x"},{"key":"963_CR11","doi-asserted-by":"publisher","first-page":"D921","DOI":"10.1093\/nar\/gku955","volume":"43","author":"Y Igarashi","year":"2015","unstructured":"Igarashi Y, Nakatsu N, Yamashita T et al (2015) Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res 43:D921-927. https:\/\/doi.org\/10.1093\/nar\/gku955","journal-title":"Nucleic Acids Res"},{"key":"963_CR12","unstructured":"NBCI Taxonomy https:\/\/www.ncbi.nlm.nih.gov\/taxonomy. Accessed 16 Jan 2025"},{"key":"963_CR13","unstructured":"DRUGMATRIX https:\/\/cebs.niehs.nih.gov\/cebs\/paper\/15670. Accessed 16 Jan 2025"},{"key":"963_CR14","unstructured":"Open TG-GATEs http:\/\/togodb.biosciencedbc.jp\/togodb\/view\/open_tggates_main#en. Accessed 16 Jan 2025"},{"key":"963_CR15","unstructured":"United States Adopted Names (USAN) https:\/\/www.ama-assn.org\/about\/united-states-adopted-names. Accessed 16 Jan 2025"},{"key":"963_CR16","unstructured":"Lists of Recommended and Proposed INNs https:\/\/www.who.int\/teams\/health-product-and-policy-standards\/inn\/inn-lists. Accessed 16 Jan 2025"},{"key":"963_CR17","unstructured":"The Malaria Box, Medicines for Malaria Venture (MMV) http:\/\/www.mmv.org\/malariabox. Accessed 16 Jan 2025"},{"key":"963_CR18","unstructured":"FAQs, ChEMBL Interface Documentation https:\/\/chembl.gitbook.io\/chembl-interface-documentation\/frequently-asked-questions\/chembl-data-questions. Accessed 16 Jan 2025"},{"key":"963_CR19","unstructured":"The Cell Line Ontology (CLO) http:\/\/bioportal.bioontology.org\/ontologies\/CLO. Accessed 16 Jan 2025"},{"key":"963_CR20","unstructured":"The Experimental Factor Ontology (EFO) http:\/\/bioportal.bioontology.org\/ontologies\/EFO. Accessed 16 Jan 2025"},{"key":"963_CR21","doi-asserted-by":"publisher","first-page":"25","DOI":"10.7171\/jbt.18-2902-002","volume":"29","author":"A Bairoch","year":"2018","unstructured":"Bairoch A (2018) The Cellosaurus, a cell-line knowledge resource. J Biomol Tech 29:25\u201338. https:\/\/doi.org\/10.7171\/jbt.18-2902-002","journal-title":"J Biomol Tech"},{"key":"963_CR22","unstructured":"The BioAssay Ontology (BAO) http:\/\/bioassayontology.org\/. Accessed 16 Jan 2025"},{"key":"963_CR23","unstructured":"The ATC index https:\/\/atcddd.fhi.no\/atc_ddd_index\/. Accessed 16 Jan 2025"},{"key":"963_CR24","unstructured":"Knowledge Management Center for Illuminating the Druggable Genome https:\/\/druggablegenome.net\/KMC_UNM. Accessed 16 Jan 2025"},{"key":"963_CR25","doi-asserted-by":"publisher","DOI":"10.7717\/peerj.15153","volume":"11","author":"MP Magari\u00f1os","year":"2023","unstructured":"Magari\u00f1os MP, Gaulton A, F\u00e9lix E et al (2023) Illuminating the druggable genome through patent bioactivity data. PeerJ 11:e15153. https:\/\/doi.org\/10.7717\/peerj.15153","journal-title":"PeerJ"},{"key":"963_CR26","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1021\/acs.chemrestox.0c00296","volume":"34","author":"FMI Hunter","year":"2021","unstructured":"Hunter FMI, Bento AP, Bosc N et al (2021) Drug safety data curation and modeling in ChEMBL: boxed warnings and withdrawn drugs. Chem Res Toxicol 34:385\u2013395. https:\/\/doi.org\/10.1021\/acs.chemrestox.0c00296","journal-title":"Chem Res Toxicol"},{"key":"963_CR27","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s13321-018-0325-4","volume":"11","author":"N Bosc","year":"2019","unstructured":"Bosc N, Atkinson F, Felix E et al (2019) Large scale comparison of QSAR and conformal prediction methods and their applications in drug discovery. J Cheminform 11:4. https:\/\/doi.org\/10.1186\/s13321-018-0325-4","journal-title":"J Cheminform"},{"key":"963_CR28","doi-asserted-by":"publisher","DOI":"10.1039\/d4md00735b","author":"C Tredup","year":"2024","unstructured":"Tredup C, Ackloo S, Beck H et al (2024) Toward target 2035: EUbOPEN\u2014a public-private partnership to enable & unlock biology in the open. RSC Med Chem. https:\/\/doi.org\/10.1039\/d4md00735b","journal-title":"RSC Med Chem"},{"key":"963_CR29","unstructured":"Downloads, ChEMBL Interface Documentation https:\/\/chembl.gitbook.io\/chembl-interface-documentation\/downloads. Accessed 16 Jan 2025"},{"key":"963_CR30","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 et al (2024) The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Res 52:D1180\u2013D1192. https:\/\/doi.org\/10.1093\/nar\/gkad1004","journal-title":"Nucleic Acids Res"},{"key":"963_CR31","doi-asserted-by":"publisher","first-page":"2068","DOI":"10.3390\/nano10102068","volume":"10","author":"A Ammar","year":"2020","unstructured":"Ammar A, Bonaretti S, Winckers L et al (2020) A semi-automated workflow for FAIR maturity indicators in the life sciences. Nanomaterials (Basel) 10:2068. https:\/\/doi.org\/10.3390\/nano10102068","journal-title":"Nanomaterials (Basel)"},{"key":"963_CR32","doi-asserted-by":"publisher","first-page":"596","DOI":"10.3762\/bjoc.16.54","volume":"16","author":"C Southan","year":"2020","unstructured":"Southan C (2020) Opening up connectivity between documents, structures and bioactivity. Beilstein J Org Chem 16:596\u2013606. https:\/\/doi.org\/10.3762\/bjoc.16.54","journal-title":"Beilstein J Org Chem"},{"key":"963_CR33","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/1758-2946-5-33","volume":"5","author":"L Rosenbaum","year":"2013","unstructured":"Rosenbaum L, D\u00f6rr A, Bauer MR et al (2013) Inferring multi-target QSAR models with taxonomy-based multi-task learning. J Cheminform 5:33. https:\/\/doi.org\/10.1186\/1758-2946-5-33","journal-title":"J Cheminform"},{"key":"963_CR34","doi-asserted-by":"publisher","first-page":"5416","DOI":"10.1016\/j.bmc.2012.02.034","volume":"20","author":"E Lounkine","year":"2012","unstructured":"Lounkine E, Kutchukian P, Petrone P et al (2012) Chemotography for multi-target SAR analysis in the context of biological pathways. Bioorg Med Chem 20:5416\u20135427. https:\/\/doi.org\/10.1016\/j.bmc.2012.02.034","journal-title":"Bioorg Med Chem"},{"key":"963_CR35","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/978-1-4939-8639-2_11","volume":"1825","author":"Y Hu","year":"2018","unstructured":"Hu Y, Bajorath J (2018) SAR matrix method for large-scale analysis of compound structure\u2013activity relationships and exploration of multitarget activity spaces. Methods Mol Biol 1825:339\u2013352. https:\/\/doi.org\/10.1007\/978-1-4939-8639-2_11","journal-title":"Methods Mol Biol"},{"key":"963_CR36","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0199348","volume":"13","author":"T Sato","year":"2018","unstructured":"Sato T, Yuki H, Ogura K, Honma T (2018) Construction of an integrated database for hERG blocking small molecules. PLoS ONE 13:e0199348. https:\/\/doi.org\/10.1371\/journal.pone.0199348","journal-title":"PLoS ONE"},{"key":"963_CR37","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1016\/j.drudis.2011.10.005","volume":"16","author":"S Muresan","year":"2011","unstructured":"Muresan S, Petrov P, Southan C et al (2011) Making every SAR point count: the development of Chemistry Connect for the large-scale integration of structure and bioactivity data. Drug Discov Today 16:1019\u20131030. https:\/\/doi.org\/10.1016\/j.drudis.2011.10.005","journal-title":"Drug Discov Today"},{"key":"963_CR38","doi-asserted-by":"publisher","first-page":"1811","DOI":"10.1021\/acs.jcim.8b00466","volume":"59","author":"A T\u00fcrkov\u00e1","year":"2019","unstructured":"T\u00fcrkov\u00e1 A, Jain S, Zdrazil B (2019) Integrative data mining, scaffold analysis, and sequential binary classification models for exploring ligand profiles of hepatic organic anion transporting polypeptides. J Chem Inf Model 59:1811\u20131825. https:\/\/doi.org\/10.1021\/acs.jcim.8b00466","journal-title":"J Chem Inf Model"},{"key":"963_CR39","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1186\/s13321-015-0098-y","volume":"7","author":"LH Mervin","year":"2015","unstructured":"Mervin LH, Afzal AM, Drakakis G et al (2015) Target prediction utilising negative bioactivity data covering large chemical space. J Cheminform 7:51. https:\/\/doi.org\/10.1186\/s13321-015-0098-y","journal-title":"J Cheminform"},{"key":"963_CR40","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.4c01830","author":"T-H Wei","year":"2024","unstructured":"Wei T-H, Zhou S-S, Jing X-L et al (2024) Kinase-bench: comprehensive benchmarking tools and guidance for achieving selectivity in kinase drug discovery. J Chem Inf Model. https:\/\/doi.org\/10.1021\/acs.jcim.4c01830","journal-title":"J Chem Inf Model"},{"key":"963_CR41","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1186\/s13321-017-0232-0","volume":"9","author":"EB Lenselink","year":"2017","unstructured":"Lenselink EB, ten Dijke N, Bongers B et al (2017) Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set. J Cheminform 9:45. https:\/\/doi.org\/10.1186\/s13321-017-0232-0","journal-title":"J Cheminform"},{"key":"963_CR42","doi-asserted-by":"publisher","first-page":"3524","DOI":"10.1021\/acs.jcim.2c00744","volume":"62","author":"T Akhmetshin","year":"2022","unstructured":"Akhmetshin T, Lin A, Mazitov D et al (2022) HyFactor: a novel open-source, graph-based architecture for chemical structure generation. J Chem Inf Model 62:3524\u20133534. https:\/\/doi.org\/10.1021\/acs.jcim.2c00744","journal-title":"J Chem Inf Model"},{"key":"963_CR43","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1007\/s11030-022-10467-9","volume":"27","author":"R Yang","year":"2023","unstructured":"Yang R, Zhao G, Cheng B, Yan B (2023) Identification of potential matrix metalloproteinase-2 inhibitors from natural products through advanced machine learning-based cheminformatics approaches. Mol Divers 27:1053\u20131066. https:\/\/doi.org\/10.1007\/s11030-022-10467-9","journal-title":"Mol Divers"},{"key":"963_CR44","doi-asserted-by":"publisher","first-page":"26551","DOI":"10.1021\/acsomega.0c03302","volume":"5","author":"PA Vignaux","year":"2020","unstructured":"Vignaux PA, Minerali E, Foil DH et al (2020) Machine learning for discovery of GSK3\u03b2 inhibitors. ACS Omega 5:26551\u201326561. https:\/\/doi.org\/10.1021\/acsomega.0c03302","journal-title":"ACS Omega"},{"key":"963_CR45","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.ejmech.2019.01.010","volume":"165","author":"I Casciuc","year":"2019","unstructured":"Casciuc I, Horvath D, Gryniukova A et al (2019) Pros and cons of virtual screening based on public \u201cBig Data\u201d: in silico mining for new bromodomain inhibitors. Eur J Med Chem 165:258\u2013272. https:\/\/doi.org\/10.1016\/j.ejmech.2019.01.010","journal-title":"Eur J Med Chem"},{"key":"963_CR46","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.1c01460","author":"A Tuerkova","year":"2022","unstructured":"Tuerkova A, Bongers BJ, Norinder U et al (2022) Identifying novel inhibitors for hepatic organic anion transporting polypeptides by machine learning-based virtual screening. J Chem Inf Model. https:\/\/doi.org\/10.1021\/acs.jcim.1c01460","journal-title":"J Chem Inf Model"},{"key":"963_CR47","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2022.951083","volume":"13","author":"P Delre","year":"2022","unstructured":"Delre P, Lavado GJ, Lamanna G et al (2022) Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques. Front Pharmacol 13:951083. https:\/\/doi.org\/10.3389\/fphar.2022.951083","journal-title":"Front Pharmacol"},{"key":"963_CR48","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1186\/s13321-024-00917-x","volume":"16","author":"D Gadaleta","year":"2024","unstructured":"Gadaleta D, Garcia de Lomana M, Serrano-Candelas E et al (2024) Quantitative structure\u2013activity relationships of chemical bioactivity toward proteins associated with molecular initiating events of organ-specific toxicity. J Cheminf 16:122. https:\/\/doi.org\/10.1186\/s13321-024-00917-x","journal-title":"J Cheminf"},{"key":"963_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoenv.2024.117352","volume":"288","author":"Y Hong","year":"2024","unstructured":"Hong Y, Wang D, Lin Y et al (2024) Environmental triggers and future risk of developing autoimmune diseases: molecular mechanism and network toxicology analysis of bisphenol A. Ecotoxicol Environ Saf 288:117352. https:\/\/doi.org\/10.1016\/j.ecoenv.2024.117352","journal-title":"Ecotoxicol Environ Saf"},{"key":"963_CR50","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.230","volume":"5","author":"FMI Hunter","year":"2018","unstructured":"Hunter FMI, Atkinson LF, Bento AP et al (2018) A large-scale dataset of in vivo pharmacology assay results. Sci Data 5:180230. https:\/\/doi.org\/10.1038\/sdata.2018.230","journal-title":"Sci Data"},{"key":"963_CR51","doi-asserted-by":"publisher","first-page":"2647","DOI":"10.1021\/ci500361u","volume":"54","author":"J Alvarsson","year":"2014","unstructured":"Alvarsson J, Eklund M, Engkvist O et al (2014) Ligand-based target prediction with signature fingerprints. J Chem Inf Model 54:2647\u20132653. https:\/\/doi.org\/10.1021\/ci500361u","journal-title":"J Chem Inf Model"},{"key":"963_CR52","doi-asserted-by":"publisher","DOI":"10.1002\/minf.202100106","volume":"41","author":"A Abdulhakeem Mansour Alhasbary","year":"2022","unstructured":"Abdulhakeem Mansour Alhasbary A, Hashimah Ahamed Hassain Malim N (2022) Turbo similarity searching: effect of partial ranking and fusion rules on ChEMBL database. Mol Inform 41:e2100106. https:\/\/doi.org\/10.1002\/minf.202100106","journal-title":"Mol Inform"},{"key":"963_CR53","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1186\/s13321-016-0148-0","volume":"8","author":"NM O\u2019Boyle","year":"2016","unstructured":"O\u2019Boyle NM, Sayle RA (2016) Comparing structural fingerprints using a literature-based similarity benchmark. J Cheminform 8:36. https:\/\/doi.org\/10.1186\/s13321-016-0148-0","journal-title":"J Cheminform"},{"key":"963_CR54","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1080\/1062936X.2015.1078407","volume":"26","author":"PV Pogodin","year":"2015","unstructured":"Pogodin PV, Lagunin AA, Filimonov DA, Poroikov VV (2015) PASS targets: ligand-based multi-target computational system based on a public data and na\u00efve Bayes approach. SAR QSAR Environ Res 26:783\u2013793. https:\/\/doi.org\/10.1080\/1062936X.2015.1078407","journal-title":"SAR QSAR Environ Res"},{"key":"963_CR55","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1186\/s12859-017-1586-z","volume":"18","author":"T Huang","year":"2017","unstructured":"Huang T, Mi H, Lin C-Y et al (2017) MOST: most-similar ligand based approach to target prediction. BMC Bioinformatics 18:165. https:\/\/doi.org\/10.1186\/s12859-017-1586-z","journal-title":"BMC Bioinformatics"},{"key":"963_CR56","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1208\/s12248-012-9449-z","volume":"15","author":"L Wang","year":"2013","unstructured":"Wang L, Ma C, Wipf P et al (2013) TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database. AAPS J 15:395\u2013406. https:\/\/doi.org\/10.1208\/s12248-012-9449-z","journal-title":"AAPS J"},{"key":"963_CR57","doi-asserted-by":"publisher","first-page":"4868","DOI":"10.1021\/acs.jcim.1c00498","volume":"61","author":"F Ciriaco","year":"2021","unstructured":"Ciriaco F, Gambacorta N, Alberga D, Nicolotti O (2021) Quantitative polypharmacology profiling based on a multifingerprint similarity predictive approach. J Chem Inf Model 61:4868\u20134876. https:\/\/doi.org\/10.1021\/acs.jcim.1c00498","journal-title":"J Chem Inf Model"},{"key":"963_CR58","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1021\/acs.jcim.8b00524","volume":"59","author":"M Awale","year":"2019","unstructured":"Awale M, Reymond J-L (2019) Polypharmacology Browser PPB2: target prediction combining nearest neighbors with machine learning. J Chem Inf Model 59:10\u201317. https:\/\/doi.org\/10.1021\/acs.jcim.8b00524","journal-title":"J Chem Inf Model"},{"key":"963_CR59","doi-asserted-by":"publisher","first-page":"2181","DOI":"10.1021\/ci300047k","volume":"52","author":"R Guha","year":"2012","unstructured":"Guha R (2012) Exploring uncharted territories: predicting activity cliffs in structure\u2013activity landscapes. J Chem Inf Model 52:2181\u20132191. https:\/\/doi.org\/10.1021\/ci300047k","journal-title":"J Chem Inf Model"},{"key":"963_CR60","doi-asserted-by":"publisher","first-page":"1806","DOI":"10.1021\/ci300274c","volume":"52","author":"Y Hu","year":"2012","unstructured":"Hu Y, Bajorath J (2012) Extending the activity cliff concept: structural categorization of activity cliffs and systematic identification of different types of cliffs in the ChEMBL database. J Chem Inf Model 52:1806\u20131811. https:\/\/doi.org\/10.1021\/ci300274c","journal-title":"J Chem Inf Model"},{"key":"963_CR61","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.2533\/chimia.2022.1045","volume":"76","author":"J-L Reymond","year":"2022","unstructured":"Reymond J-L (2022) Molecular similarity for drug discovery, target prediction and chemical space visualization. Chimia (Aarau) 76:1045\u20131051. https:\/\/doi.org\/10.2533\/chimia.2022.1045","journal-title":"Chimia (Aarau)"},{"key":"963_CR62","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.2533\/chimia.2019.1018","volume":"73","author":"J Ar\u00fas-Pous","year":"2019","unstructured":"Ar\u00fas-Pous J, Awale M, Probst D, Reymond J-L (2019) Exploring chemical space with machine learning. Chimia (Aarau) 73:1018\u20131023. https:\/\/doi.org\/10.2533\/chimia.2019.1018","journal-title":"Chimia (Aarau)"},{"key":"963_CR63","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1080\/1062936X.2024.2304803","volume":"35","author":"PV Pogodin","year":"2024","unstructured":"Pogodin PV, Salina EG, Semenov VV et al (2024) Ligand-based virtual screening and biological evaluation of inhibitors of Mycobacterium tuberculosis H37Rv. SAR QSAR Environ Res 35:53\u201369. https:\/\/doi.org\/10.1080\/1062936X.2024.2304803","journal-title":"SAR QSAR Environ Res"},{"key":"963_CR64","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1080\/1062936X.2024.2392677","volume":"35","author":"Y Chongjun","year":"2024","unstructured":"Chongjun Y, Nasr AMS, Latif MAM et al (2024) Predicting repurposed drugs targeting the NS3 protease of dengue virus using machine learning-based QSAR, molecular docking, and molecular dynamics simulations. SAR QSAR Environ Res 35:707\u2013728. https:\/\/doi.org\/10.1080\/1062936X.2024.2392677","journal-title":"SAR QSAR Environ Res"},{"key":"963_CR65","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0012690","volume":"18","author":"S Fernandes Silva","year":"2024","unstructured":"Fernandes Silva S, Hollunder Klippel A, Sigurdard\u00f3ttir S et al (2024) An experimental target-based platform in yeast for screening Plasmodium vivax deoxyhypusine synthase inhibitors. PLoS Negl Trop Dis 18:e0012690. https:\/\/doi.org\/10.1371\/journal.pntd.0012690","journal-title":"PLoS Negl Trop Dis"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-00963-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-025-00963-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-025-00963-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T18:03:06Z","timestamp":1754935386000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-025-00963-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,10]]},"references-count":65,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["963"],"URL":"https:\/\/doi.org\/10.1186\/s13321-025-00963-z","relation":{"is-referenced-by":[{"id-type":"doi","id":"10.1007\/s10967-026-10743-0","asserted-by":"object"}]},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,10]]},"assertion":[{"value":"21 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2025","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This article has been updated to amend the license information","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Barbara Zdrazil is Co-Editor in Chief of the Journal of Cheminformatics and ChEMBL Group Coordinator at the European Bioinformatics Institute (EMBL-EBI).\u00a0B. Z. did not participate in the peer review or decision making process for this article. The opinions reflected in the review article do reflect the opinions of the author and not necessarily of the employer (EMBL-EBI). ChatGPT-4 was used to re-formulate some of the sentences that needed correction as suggested by the reviewers.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"32"}}