{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T03:18:19Z","timestamp":1784258299074,"version":"3.55.0"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T00:00:00Z","timestamp":1672185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272105"],"award-info":[{"award-number":["62272105"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Finding molecules with desired pharmaceutical properties is crucial in drug discovery. Generative models can be an efficient tool to find desired molecules through the distribution learned by the model to approximate given training data. Existing generative models (i) do not consider backbone structures (scaffolds), resulting in inefficiency or (ii) need prior patterns for scaffolds, causing bias. Scaffolds are reasonable to use, and it is imperative to design a generative model without any prior scaffold patterns.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a generative model-based molecule generator, Sc2Mol, without any prior scaffold patterns. Sc2Mol uses SMILES strings for molecules. It consists of two steps: scaffold generation and scaffold decoration, which are carried out by a variational autoencoder and a transformer, respectively. The two steps are powerful for implementing random molecule generation and scaffold optimization. Our empirical evaluation using drug-like molecule datasets confirmed the success of our model in distribution learning and molecule optimization. Also, our model could automatically learn the rules to transform coarse scaffolds into sophisticated drug candidates. These rules were consistent with those for current lead optimization.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The code is available at https:\/\/github.com\/zhiruiliao\/Sc2Mol.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac814","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:02:25Z","timestamp":1672221745000},"source":"Crossref","is-referenced-by-count":23,"title":["Sc2Mol: a scaffold-based two-step molecule generator with variational autoencoder and transformer"],"prefix":"10.1093","volume":"39","author":[{"given":"Zhirui","family":"Liao","sequence":"first","affiliation":[{"name":"School of Computer Science, Fudan University , Shanghai 200433, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hunter College, The City University of New York , New York, NY 10065, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroshi","family":"Mamitsuka","sequence":"additional","affiliation":[{"name":"Bioinformatics Center, Institute for Chemical Research, Kyoto University , Uji, Kyoto Prefecture 611-0011, Japan"},{"name":"Department of Computer Science, Aalto University , Espoo 00076, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6067-5312","authenticated-orcid":false,"given":"Shanfeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University , Shanghai 200433, China"},{"name":"Shanghai Qi Zhi Institute , Shanghai 200030, China"},{"name":"Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education , Shanghai 200433, China"},{"name":"Shanghai Key Lab of Intelligent Information Processing and Shanghai Institute of Artificial Intelligence Algorithm, Fudan University , Shanghai 200433, China"},{"name":"Zhangjiang Fudan International Innovation Center , Shanghai 200433, China"},{"name":"Institute of Artificial Intelligence Biomedicine, Nanjing University , Nanjing, Jiangsu 210031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,12,28]]},"reference":[{"key":"2023011212505614600_btac814-B1","first-page":"214","author":"Arjovsky","year":"2017"},{"key":"2023011212505614600_btac814-B2","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1186\/s13321-020-00441-8","article-title":"Smiles-based deep generative scaffold decorator for de-novo drug design","volume":"12","author":"Ar\u00fas-Pous","year":"2020","journal-title":"J. Cheminform"},{"key":"2023011212505614600_btac814-B3","doi-asserted-by":"crossref","first-page":"2719","DOI":"10.1021\/jm901137j","article-title":"New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays","volume":"53","author":"Baell","year":"2010","journal-title":"J. Med. Chem"},{"key":"2023011212505614600_btac814-B4","doi-asserted-by":"crossref","first-page":"3307","DOI":"10.1016\/j.bmcl.2014.06.003","article-title":"Discovery of vu0431316: a negative allosteric modulator of mglu5 with activity in a mouse model of anxiety","volume":"24","author":"Bates","year":"2014","journal-title":"Bioorg. Med. Chem. Lett"},{"key":"2023011212505614600_btac814-B5","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"},{"key":"2023011212505614600_btac814-B6","doi-asserted-by":"crossref","first-page":"5918","DOI":"10.1021\/acs.jcim.0c00915","article-title":"Reinvent 2.0: an AI tool for de novo drug design","volume":"60","author":"Blaschke","year":"2020","journal-title":"J. Chem. Inf. Model"},{"key":"2023011212505614600_btac814-B7","first-page":"10","author":"Bowman","year":"2016"},{"key":"2023011212505614600_btac814-B8","doi-asserted-by":"crossref","first-page":"9442","DOI":"10.1021\/acs.jmedchem.8b00675","article-title":"Where do recent small molecule clinical development candidates come from?","volume":"61","author":"Brown","year":"2018","journal-title":"J. Med. Chem"},{"key":"2023011212505614600_btac814-B9","volume-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"Chung","year":"2014"},{"key":"2023011212505614600_btac814-B10","first-page":"933","author":"Dauphin","year":"2017"},{"key":"2023011212505614600_btac814-B11","author":"Dong","year":"2018"},{"key":"2023011212505614600_btac814-B12","doi-asserted-by":"crossref","first-page":"5072","DOI":"10.1021\/acs.jmedchem.7b00410","article-title":"Discovery of n-(5-fluoropyridin-2-yl)-6-methyl-4-(pyrimidin-5-yloxy)picolinamide (vu0424238): a novel negative allosteric modulator of metabotropic glutamate receptor subtype 5 selected for clinical evaluation","volume":"60","author":"Felts","year":"2017","journal-title":"J. Med. Chem"},{"key":"2023011212505614600_btac814-B13","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","article-title":"Automatic chemical design using a data-driven continuous representation of molecules","volume":"4","author":"G\u00f3mez-Bombarelli","year":"2018","journal-title":"ACS Cent. Sci"},{"key":"2023011212505614600_btac814-B14","first-page":"770","author":"He","year":"2016"},{"key":"2023011212505614600_btac814-B15","first-page":"2323","author":"Jin","year":"2018"},{"key":"2023011212505614600_btac814-B16","author":"Karras","year":"2020"},{"key":"2023011212505614600_btac814-B17","author":"Kingma","year":"2015"},{"key":"2023011212505614600_btac814-B18","author":"Kingma","year":"2014"},{"key":"2023011212505614600_btac814-B19","doi-asserted-by":"crossref","first-page":"5637","DOI":"10.1021\/acs.jcim.0c01015","article-title":"Scaffold-constrained molecular generation","volume":"60","author":"Langevin","year":"2020","journal-title":"J. Chem. Inf. Model"},{"key":"2023011212505614600_btac814-B20","volume-title":"Layer normalization","author":"Lei Ba","year":"2016"},{"key":"2023011212505614600_btac814-B21","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1021\/acs.jcim.9b00727","article-title":"Deepscaffold: a comprehensive tool for scaffold-based de novo drug discovery using deep learning","volume":"60","author":"Li","year":"2020","journal-title":"J. Chem. Inf. Model"},{"key":"2023011212505614600_btac814-B22","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1039\/C9SC04503A","article-title":"Scaffold-based molecular design with a graph generative model","volume":"11","author":"Lim","year":"2020","journal-title":"Chem. Sci"},{"key":"2023011212505614600_btac814-B23","doi-asserted-by":"crossref","first-page":"3552","DOI":"10.1021\/acs.jmedchem.6b01807","article-title":"The necessary nitrogen atom: a versatile high-impact design element for multiparameter optimization","volume":"60","author":"Pennington","year":"2017","journal-title":"J. Med. Chem"},{"key":"2023011212505614600_btac814-B24","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":"2023011212505614600_btac814-B25","doi-asserted-by":"crossref","DOI":"10.3389\/fphar.2020.565644","article-title":"Molecular sets (MOSES): a benchmarking platform for molecular generation models","volume":"11","author":"Polykovskiy","year":"2020","journal-title":"Front. Pharmacol"},{"key":"2023011212505614600_btac814-B26","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.ddtec.2020.09.003","article-title":"On failure modes in molecule generation and optimization","volume":"32-33","author":"Renz","year":"2019","journal-title":"Drug Discov. Today. Technol"},{"key":"2023011212505614600_btac814-B27","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1021\/ci100050t","article-title":"Extended-connectivity fingerprints","volume":"50","author":"Rogers","year":"2010","journal-title":"J. Chem. Inf. Model"},{"key":"2023011212505614600_btac814-B28","first-page":"1929","article-title":"Dropout: a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res"},{"key":"2023011212505614600_btac814-B29","doi-asserted-by":"crossref","first-page":"2324","DOI":"10.1021\/acs.jcim.5b00559","article-title":"Zinc 15 \u2013 ligand discovery for everyone","volume":"55","author":"Sterling","year":"2015","journal-title":"J. Chem. Inf. Model"},{"key":"2023011212505614600_btac814-B30","volume-title":"Advances in Neural Information Processing Systems","author":"Vaswani","year":"2017"},{"key":"2023011212505614600_btac814-B31","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"Smiles, a chemical language and information system. 1. Introduction to methodology and encoding rules","volume":"28","author":"Weininger","year":"1988","journal-title":"J. Chem. Inf. Model"},{"key":"2023011212505614600_btac814-B32","doi-asserted-by":"crossref","first-page":"868","DOI":"10.1021\/ci990307l","article-title":"Prediction of physicochemical parameters by atomic contributions","volume":"39","author":"Wildman","year":"1999","journal-title":"J. Chem. Inf. Comput. Sci"},{"key":"2023011212505614600_btac814-B33","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1162\/neco.1989.1.2.270","article-title":"A learning algorithm for continually running fully recurrent neural networks","volume":"1","author":"Williams","year":"1989","journal-title":"Neural Comput"},{"key":"2023011212505614600_btac814-B34","doi-asserted-by":"crossref","first-page":"8312","DOI":"10.1039\/D0SC03126G","article-title":"Syntalinker: automatic fragment linking with deep conditional transformer neural networks","volume":"11","author":"Yang","year":"2020","journal-title":"Chem. Sci"},{"key":"2023011212505614600_btac814-B35","doi-asserted-by":"crossref","first-page":"6421","DOI":"10.1021\/acs.jmedchem.8b00180","article-title":"Mapping the efficiency and physicochemical trajectories of successful optimizations","volume":"61","author":"Young","year":"2018","journal-title":"J. Med. Chem"},{"key":"2023011212505614600_btac814-B36","first-page":"617","author":"Zang","year":"2020"},{"key":"2023011212505614600_btac814-B37","first-page":"3721","author":"Zhang","year":"2019"},{"key":"2023011212505614600_btac814-B38","first-page":"1364","author":"Zhang","year":"2017"},{"key":"2023011212505614600_btac814-B39","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/1-4020-4407-0_6","volume-title":"Scaffold-Based Drug Discovery","author":"Zhang","year":"2007"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btac814\/48437395\/btac814.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/1\/btac814\/48646689\/btac814.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/39\/1\/btac814\/48646689\/btac814.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T17:44:26Z","timestamp":1673545466000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btac814\/6964383"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2022,12,28]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btac814","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,1,1]]},"published":{"date-parts":[[2022,12,28]]},"article-number":"btac814"}}