{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T20:09:54Z","timestamp":1778875794313,"version":"3.51.4"},"reference-count":20,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T00:00:00Z","timestamp":1718841600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006565","name":"University of Johannesburg","doi-asserted-by":"publisher","award":["201212831"],"award-info":[{"award-number":["201212831"]}],"id":[{"id":"10.13039\/501100006565","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Data in Brief"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1016\/j.dib.2024.110677","type":"journal-article","created":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T19:57:09Z","timestamp":1719431829000},"page":"110677","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"special_numbering":"C","title":["Dataset for discovering new hypertension small molecules using machine learning-aided computational fragment-based design"],"prefix":"10.1016","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5770-1412","authenticated-orcid":false,"given":"Odifentse Mapula-e","family":"Lehasa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Uche A.K.","family":"Chude-Okonkwo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.dib.2024.110677_bib0001","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1152\/physiol.00026.2016","article-title":"The hypertension pandemic: an evolutionary perspective","volume":"32","author":"Rossier","year":"2017","journal-title":"Physiology"},{"issue":"1","key":"10.1016\/j.dib.2024.110677_bib0002","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12889-022-12562-w","article-title":"Association between comorbidity and health-related quality of life in a hypertensive population: a hospital-based study in Bangladesh","volume":"22","author":"Mannan","year":"2022","journal-title":"BMC Public Health"},{"key":"10.1016\/j.dib.2024.110677_bib0003","unstructured":"World Health Organization (WHO), \u201cHypertension\u201d [Online]. Available: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/hypertension."},{"issue":"5","key":"10.1016\/j.dib.2024.110677_bib0004","doi-asserted-by":"crossref","first-page":"E53","DOI":"10.1161\/HYP.0000000000000084","article-title":"Resistant hypertension: detection, evaluation, and management a scientific statement from the American heart association","volume":"72","author":"Carey","year":"2018","journal-title":"Hypertension"},{"issue":"7787","key":"10.1016\/j.dib.2024.110677_bib0005","doi-asserted-by":"crossref","first-page":"S49","DOI":"10.1038\/d41586-019-03846-0","article-title":"Hunting for new drugs with AI","volume":"576","author":"Freedman","year":"2019","journal-title":"Nature"},{"key":"10.1016\/j.dib.2024.110677_bib0006","article-title":"Data for machine learning-aided computational fragment-based design of small molecules for hypertension treatment","volume":"1","author":"Lehasa","year":"2024","journal-title":"Mendeley Data"},{"key":"10.1016\/j.dib.2024.110677_bib0007","unstructured":"O.M. Lehasa and U.A.K. Chude-Okonkwo, \u201cComputational-FBDD-for-hypertension: version 2.\u201d Zenodo, 2024. 10.5281\/zenodo.11636007."},{"key":"10.1016\/j.dib.2024.110677_bib0008","series-title":"Fragment-based Approaches in Drug Discovery","author":"Jahnke","year":"2006"},{"key":"10.1016\/j.dib.2024.110677_bib0009","unstructured":"USA Food and Drug Administration (FDA), \u201cQuick guide to creating a structure-data file (SD File) for DMF submissions disclaimer.\u201d [Online]. Available: https:\/\/www.fda.gov\/media\/151718\/download#:\u223c:text=sdf\u201d%2C\u201cSDFile\u201d,bonds%2Cconnectivity%2Candcoordinates."},{"issue":"4","key":"10.1016\/j.dib.2024.110677_bib0010","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.ddtec.2004.11.007","article-title":"Lead- and drug-like compounds: the rule-of-five revolution","volume":"1","author":"Lipinski","year":"2004","journal-title":"Drug Discov. Today Technol."},{"issue":"2","key":"10.1016\/j.dib.2024.110677_bib0011","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1038\/nrd4163","article-title":"The role of ligand efficiency metrics in drug discovery","volume":"13","author":"Hopkins","year":"2014","journal-title":"Nat. Rev. Drug Discov."},{"issue":"2","key":"10.1016\/j.dib.2024.110677_bib0012","doi-asserted-by":"crossref","first-page":"501","DOI":"10.3390\/encyclopedia3020035","article-title":"Molecular filters in medicinal chemistry","volume":"3","author":"Kralj","year":"2023","journal-title":"Encyclopedia"},{"issue":"3\u20134","key":"10.1016\/j.dib.2024.110677_bib0013","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.drudis.2010.11.014","article-title":"The impact of aromatic ring count on compound developability: further insights by examining carbo- and hetero-aromatic and -aliphatic ring types","volume":"16","author":"Ritchie","year":"2011","journal-title":"Drug Discov. Today"},{"issue":"2","key":"10.1016\/j.dib.2024.110677_bib0014","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1038\/nchem.1243","article-title":"Quantifying the chemical beauty of drugs","volume":"4","author":"Bickerton","year":"2012","journal-title":"Nat. Chem."},{"issue":"8","key":"10.1016\/j.dib.2024.110677_bib0015","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1177\/2472555219859845","article-title":"Using physicochemical measurements to influence better compound design","volume":"24","author":"Bunally","year":"2019","journal-title":"SLAS Discov."},{"key":"10.1016\/j.dib.2024.110677_bib0016","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2022.968104","article-title":"Small molecule angiotensin converting enzyme inhibitors: a medicinal chemistry perspective","volume":"13","author":"Zheng","year":"2022","journal-title":"Front. Pharmacol."},{"key":"10.1016\/j.dib.2024.110677_bib0017","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1093\/ajhp\/57.suppl_1.S3","article-title":"Overview of the angiotensin-converting-enzyme inhibitors","volume":"57","author":"Piepho","year":"2000","journal-title":"Am. Soc. Health-Syst. Pharm."},{"issue":"12","key":"10.1016\/j.dib.2024.110677_bib0018","doi-asserted-by":"crossref","first-page":"2454","DOI":"10.1111\/bph.14213","article-title":"The non-biphenyl-tetrazole angiotensin AT 1 receptor antagonist eprosartan is a unique and robust inverse agonist of the active state of the AT 1 receptor","volume":"175","author":"Takezako","year":"2018","journal-title":"Br. J. Pharmacol."},{"issue":"10","key":"10.1016\/j.dib.2024.110677_bib0019","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1038\/hr.2010.135","article-title":"A small difference in the molecular structure of angiotensin II receptor blockers induces at 1 receptor-dependent and-independent beneficial effects","volume":"33","author":"Fujino","year":"2010","journal-title":"Hypertens. Res."},{"issue":"11","key":"10.1016\/j.dib.2024.110677_bib0020","doi-asserted-by":"crossref","DOI":"10.3390\/ijms23116114","article-title":"Molecular similarity perception based on machine-learning models","volume":"23","author":"Gandini","year":"2022","journal-title":"Int. J. Mol. Sci."}],"container-title":["Data in Brief"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352340924006449?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2352340924006449?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T19:24:59Z","timestamp":1778873099000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2352340924006449"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":20,"alternative-id":["S2352340924006449"],"URL":"https:\/\/doi.org\/10.1016\/j.dib.2024.110677","relation":{"is-supplemented-by":[{"id-type":"uri","id":"https:\/\/data.mendeley.com\/datasets\/brgzpd5wj4\/1","asserted-by":"subject"},{"id-type":"uri","id":"https:\/\/data.mendeley.com\/datasets\/brgzpd5wj4\/1","asserted-by":"subject"}]},"ISSN":["2352-3409"],"issn-type":[{"value":"2352-3409","type":"print"}],"subject":[],"published":{"date-parts":[[2024,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Dataset for discovering new hypertension small molecules using machine learning-aided computational fragment-based design","name":"articletitle","label":"Article Title"},{"value":"Data in Brief","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.dib.2024.110677","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"110677"}}