{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T17:03:04Z","timestamp":1775581384904,"version":"3.50.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T00:00:00Z","timestamp":1679529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Yun Ling Scholar Project to Wei-Lie Xiao, Project of Yunnan Characteristic Plant Screening and R&D Service CXO Platform","award":["2022YKZY001"],"award-info":[{"award-number":["2022YKZY001"]}]},{"name":"Open Foundation of Key Laboratory in Media Convergence of Yunnan Province","award":["220225201"],"award-info":[{"award-number":["220225201"]}]},{"name":"Fundamental Research Project of Yunnan Province","award":["202201AT070297"],"award-info":[{"award-number":["202201AT070297"]}]},{"name":"Fundamental Research Project of Yunnan Province","award":["201901BB050052"],"award-info":[{"award-number":["201901BB050052"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81903541"],"award-info":[{"award-number":["81903541"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82260694"],"award-info":[{"award-number":["82260694"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62262072"],"award-info":[{"award-number":["62262072"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Histones are the chief protein components of chromatin, and the chemical modifications on histones crucially influence the transcriptional state of related genes. Histone modifying enzyme (HME), responsible for adding or removing the chemical labels, has emerged as a very important class of drug target, with a few HME inhibitors launched as anti-cancerous drugs and tens of molecules under clinical trials. To accelerate the drug discovery process of HME inhibitors, machine learning-based predictive models have been developed to enrich the active molecules from vast chemical space. However, the number of compounds with known activity distributed largely unbalanced among different HMEs, particularly with many targets of less than a hundred active samples. In this case, it is difficult to build effective virtual screening models directly based on machine learning.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>To this end, we propose a new Meta-learning-based Histone Modifying Enzymes Inhibitor prediction method (MetaHMEI). Our proposed MetaHMEI first uses a self-supervised pre-training approach to obtain high-quality molecular substructure embeddings from a large unlabeled chemical dataset. Then, MetaHMEI exploits a Transformer-based encoder and meta-learning framework to build a prediction model. MetaHMEI allows the effective transfer of the prior knowledge learned from HMEs with sufficient samples to HMEs with a small number of samples, so the proposed model can produce accurate predictions for HMEs with limited data. Extensive experimental results on our collected and curated HMEs datasets show that MetaHMEI is better than other methods in the case of few-shot learning. Furthermore, we applied MetaHMEI in the virtual screening process of histone JMJD3 inhibitors and successfully obtained three small molecule inhibitors, further supporting the validity of our model.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bib\/bbad115","type":"journal-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T09:46:43Z","timestamp":1679651203000},"source":"Crossref","is-referenced-by-count":12,"title":["MetaHMEI: meta-learning for prediction of few-shot histone modifying enzyme inhibitors"],"prefix":"10.1093","volume":"24","author":[{"given":"Qi","family":"Lu","sequence":"first","affiliation":[{"name":"Yunnan University The Cloud Computing Engineering Research Center of Yunnan Province, Key Laboratory of Software Engineering of Yunnan Province, Key Laboratory of Medicinal Chemistry for Natural Resource of Ministry of Education and Yunnan Characteristic Plant Extraction Laboratory, School of Software, School of Pharmacy, , Kunming 650091 , China"}]},{"given":"Ruihan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Yunnan University The Cloud Computing Engineering Research Center of Yunnan Province, Key Laboratory of Software Engineering of Yunnan Province, Key Laboratory of Medicinal Chemistry for Natural Resource of Ministry of Education and Yunnan Characteristic Plant Extraction Laboratory, School of Software, School of Pharmacy, , Kunming 650091 , China"}]},{"given":"Hongyuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Yunnan University The Cloud Computing Engineering Research Center of Yunnan Province, Key Laboratory of Software Engineering of Yunnan Province, Key Laboratory of Medicinal Chemistry for Natural Resource of Ministry of Education and Yunnan Characteristic Plant Extraction Laboratory, School of Software, School of Pharmacy, , Kunming 650091 , China"}]},{"given":"Dongxuan","family":"Ni","sequence":"additional","affiliation":[{"name":"Yunnan University The Cloud Computing Engineering Research Center of Yunnan Province, Key Laboratory of Software Engineering of Yunnan Province, Key Laboratory of Medicinal Chemistry for Natural Resource of Ministry of Education and Yunnan Characteristic Plant Extraction Laboratory, School of Software, School of Pharmacy, , Kunming 650091 , China"}]},{"given":"Weilie","family":"Xiao","sequence":"additional","affiliation":[{"name":"Yunnan University The Cloud Computing Engineering Research Center of Yunnan Province, Key Laboratory of Software Engineering of Yunnan Province, Key Laboratory of Medicinal Chemistry for Natural Resource of Ministry of Education and Yunnan Characteristic Plant Extraction Laboratory, School of Software, School of Pharmacy, , Kunming 650091 , China"}]},{"given":"Jin","family":"Li","sequence":"additional","affiliation":[{"name":"Yunnan University The Cloud Computing Engineering Research Center of Yunnan Province, Key Laboratory of Software Engineering of Yunnan Province, Key Laboratory of Medicinal Chemistry for Natural Resource of Ministry of Education and Yunnan Characteristic Plant Extraction Laboratory, School of Software, School of Pharmacy, , Kunming 650091 , China"}]}],"member":"286","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"key":"2023052022003029400_ref1","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1038\/nrd3674","article-title":"Epigenetic protein families: a new frontier for drug discovery","volume":"11","author":"Arrowsmith","year":"2012","journal-title":"Nat Rev Drug Discov"},{"key":"2023052022003029400_ref2","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1016\/j.cell.2007.01.029","article-title":"The epigenomics of cancer","volume":"128","author":"Jones","year":"2007","journal-title":"Cell"},{"key":"2023052022003029400_ref3","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.exphem.2017.12.012","article-title":"Scriptaid inhibits cell survival, cell cycle, and promotes apoptosis in multiple myeloma 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