{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T01:58:07Z","timestamp":1769824687587,"version":"3.49.0"},"reference-count":83,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,12,21]],"date-time":"2021-12-21T00:00:00Z","timestamp":1640044800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Key Research and Development Program of Zhejiang Province","award":["2020C03010"],"award-info":[{"award-number":["2020C03010"]}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LZ19H300001"],"award-info":[{"award-number":["LZ19H300001"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2020QNA7003"],"award-info":[{"award-number":["2020QNA7003"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["21575128"],"award-info":[{"award-number":["21575128"]}],"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":["81773632"],"award-info":[{"award-number":["81773632"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Recently, deep learning (DL)-based de novo drug design represents a new trend in pharmaceutical research, and numerous DL-based methods have been developed for the generation of novel compounds with desired properties. However, a comprehensive understanding of the advantages and disadvantages of these methods is still lacking. In this study, the performances of different generative models were evaluated by analyzing the properties of the generated molecules in different scenarios, such as goal-directed (rediscovery, optimization and scaffold hopping of active compounds) and target-specific (generation of novel compounds for a given target) tasks. In overall, the DL-based models have significant advantages over the baseline models built by the traditional methods in learning the physicochemical property distributions of the training sets and may be more suitable for target-specific tasks. However, both the baselines and DL-based generative models cannot fully exploit the scaffolds of the training sets, and the molecules generated by the DL-based methods even have lower scaffold diversity than those generated by the traditional models. Moreover, our assessment illustrates that the DL-based methods do not exhibit obvious advantages over the genetic algorithm-based baselines in goal-directed tasks. We believe that our study provides valuable guidance for the effective use of generative models in de novo drug design.<\/jats:p>","DOI":"10.1093\/bib\/bbab544","type":"journal-article","created":{"date-parts":[[2021,11,26]],"date-time":"2021-11-26T20:10:14Z","timestamp":1637957414000},"source":"Crossref","is-referenced-by-count":17,"title":["Comprehensive assessment of deep generative architectures for <i>de novo<\/i> drug design"],"prefix":"10.1093","volume":"23","author":[{"given":"Mingyang","family":"Wang","sequence":"first","affiliation":[{"name":"Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China"}]},{"given":"Huiyong","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Medicinal Chemistry, China Pharmaceutical University, Nanjing 210009, Jiangsu, P. R. China"}]},{"given":"Jike","family":"Wang","sequence":"additional","affiliation":[{"name":"Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China"}]},{"given":"Jinping","family":"Pang","sequence":"additional","affiliation":[{"name":"Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China"}]},{"given":"Xin","family":"Chai","sequence":"additional","affiliation":[{"name":"Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China"}]},{"given":"Lei","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Bioinformatics and Medical Engineering, School of Electrical and Information Engineering, Jiangsu University of Technology, Changzhou 213001, Jiangsu, China"}]},{"given":"Honglin","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science & Technology, Shanghai 200237, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3604-3785","authenticated-orcid":false,"given":"Dongsheng","family":"Cao","sequence":"additional","affiliation":[{"name":"Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410013, Hunan, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7227-2580","authenticated-orcid":false,"given":"Tingjun","family":"Hou","sequence":"additional","affiliation":[{"name":"Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China"}]}],"member":"286","published-online":{"date-parts":[[2021,12,21]]},"reference":[{"key":"2022012000334519200_ref1","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1007\/s10822-013-9672-4","article-title":"Estimation of the size of drug-like chemical space based on GDB-17 data","volume":"27","author":"Polishchuk","year":"2013","journal-title":"J Comput Aided Mol Des"},{"key":"2022012000334519200_ref2","doi-asserted-by":"crossref","first-page":"W612","DOI":"10.1093\/nar\/gkv352","article-title":"ChEMBL web services: streamlining access to drug discovery data and utilities","volume":"43","author":"Davies","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2022012000334519200_ref3","doi-asserted-by":"crossref","first-page":"D1045","DOI":"10.1093\/nar\/gkv1072","article-title":"Binding DB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology","volume":"44","author":"Gilson","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2022012000334519200_ref4","doi-asserted-by":"crossref","first-page":"D1102","DOI":"10.1093\/nar\/gky1033","article-title":"PubChem 2019 update: improved access to chemical data","volume":"47","author":"Kim","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2022012000334519200_ref5","doi-asserted-by":"crossref","first-page":"2324","DOI":"10.1021\/acs.jcim.5b00559","article-title":"ZINC 15-ligand discovery for everyone","volume":"55","author":"Sterling","year":"2015","journal-title":"J Chem Inf Model"},{"key":"2022012000334519200_ref6","doi-asserted-by":"crossref","first-page":"572","DOI":"10.2174\/1381612822666151125000550","article-title":"Computer aided drug design: success and limitations","volume":"22","author":"Baig","year":"2016","journal-title":"Curr Pharm Des"},{"key":"2022012000334519200_ref7","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1208\/s12248-012-9322-0","article-title":"Structure-based virtual screening for drug discovery: a problem-centric review","volume":"14","author":"Cheng","year":"2012","journal-title":"AAPS J"},{"key":"2022012000334519200_ref8","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1080\/17460441.2019.1615435","article-title":"Approaching target selectivity by de novo drug design","volume":"14","author":"Fischer","year":"2019","journal-title":"Expert Opin Drug Discovery"},{"key":"2022012000334519200_ref9","doi-asserted-by":"crossref","first-page":"8985","DOI":"10.1016\/S0040-4020(01)86503-0","article-title":"Automatic creation of drug candidate structures based on receptor structure. 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