{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T17:20:17Z","timestamp":1785864017842,"version":"3.56.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,3,12]],"date-time":"2019-03-12T00:00:00Z","timestamp":1552348800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010665","name":"H2020 Marie Sk\u0142odowska-Curie Actions","doi-asserted-by":"publisher","award":["676434"],"award-info":[{"award-number":["676434"]}],"id":[{"id":"10.13039\/100010665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1186\/s13321-019-0341-z","type":"journal-article","created":{"date-parts":[[2019,3,13]],"date-time":"2019-03-13T14:03:38Z","timestamp":1552485818000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":148,"title":["Exploring the GDB-13 chemical space using deep generative models"],"prefix":"10.1186","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9860-2944","authenticated-orcid":false,"given":"Josep","family":"Ar\u00fas-Pous","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Blaschke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silas","family":"Ulander","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jean-Louis","family":"Reymond","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongming","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ola","family":"Engkvist","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,3,12]]},"reference":[{"key":"341_CR1","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1021\/ci0255782","volume":"43","author":"P Ertl","year":"2003","unstructured":"Ertl P (2003) Cheminformatics analysis of organic substituents: identification of the most common substituents, calculation of substituent properties, and automatic identification of drug-like bioisosteric groups. J Chem Inf Comput Sci 43:374\u2013380. \n                    https:\/\/doi.org\/10.1021\/ci0255782","journal-title":"J Chem Inf Comput Sci"},{"key":"341_CR2","doi-asserted-by":"publisher","first-page":"636","DOI":"10.1002\/cmdc.200700021","volume":"2","author":"R Deursen Van","year":"2007","unstructured":"Van Deursen R, Reymond JL (2007) Chemical space travel. ChemMedChem 2:636\u2013640. \n                    https:\/\/doi.org\/10.1002\/cmdc.200700021","journal-title":"ChemMedChem"},{"key":"341_CR3","doi-asserted-by":"publisher","first-page":"e1002380","DOI":"10.1371\/journal.pcbi.1002380","volume":"8","author":"M Hartenfeller","year":"2012","unstructured":"Hartenfeller M, Zettl H, Walter M et al (2012) Dogs: reaction-driven de novo design of bioactive compounds. PLoS Comput Biol 8:e1002380. \n                    https:\/\/doi.org\/10.1371\/journal.pcbi.1002380","journal-title":"PLoS Comput Biol"},{"key":"341_CR4","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1504\/IJCBDD.2014.061649","volume":"7","author":"JL Andersen","year":"2014","unstructured":"Andersen JL, Flamm C, Merkle D, Stadler PF (2014) Generic strategies for chemical space exploration. Int J Comput Biol Drug Des 7:225. \n                    https:\/\/doi.org\/10.1504\/IJCBDD.2014.061649","journal-title":"Int J Comput Biol Drug Des"},{"key":"341_CR5","doi-asserted-by":"publisher","first-page":"1100","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 40:1100\u20131107. \n                    https:\/\/doi.org\/10.1093\/nar\/gkr777","journal-title":"Nucleic Acids Res"},{"key":"341_CR6","doi-asserted-by":"publisher","first-page":"8732","DOI":"10.1021\/ja902302h","volume":"131","author":"LC Blum","year":"2009","unstructured":"Blum LC, Reymond JL (2009) 970 Million druglike small molecules for virtual screening in the chemical universe database GDB-13. J Am Chem Soc 131:8732\u20138733. \n                    https:\/\/doi.org\/10.1021\/ja902302h","journal-title":"J Am Chem Soc"},{"key":"341_CR7","doi-asserted-by":"publisher","first-page":"2864","DOI":"10.1021\/ci300415d","volume":"52","author":"L Ruddigkeit","year":"2012","unstructured":"Ruddigkeit L, Van Deursen R, Blum LC, Reymond JL (2012) Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17. J Chem Inf Model 52:2864\u20132875. \n                    https:\/\/doi.org\/10.1021\/ci300415d","journal-title":"J Chem Inf Model"},{"key":"341_CR8","doi-asserted-by":"publisher","first-page":"2707","DOI":"10.1021\/acs.jcim.7b00457","volume":"57","author":"R Visini","year":"2017","unstructured":"Visini R, Ar\u00fas-Pous J, Awale M, Reymond JL (2017) Virtual exploration of the ring systems chemical universe. J Chem Inf Model 57:2707\u20132718. \n                    https:\/\/doi.org\/10.1021\/acs.jcim.7b00457","journal-title":"J Chem Inf Model"},{"key":"341_CR9","doi-asserted-by":"publisher","first-page":"722","DOI":"10.1021\/ar500432k","volume":"48","author":"JL Reymond","year":"2015","unstructured":"Reymond JL (2015) The chemical space project. Acc Chem Res 48:722\u2013730. \n                    https:\/\/doi.org\/10.1021\/ar500432k","journal-title":"Acc Chem Res"},{"key":"341_CR10","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu W, Jia Y et al (2015) Going deeper with convolutions. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition 07\u201312\u2013June, pp 1\u20139. \n                    https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"341_CR11","doi-asserted-by":"publisher","unstructured":"Taigman Y, Yang M, Ranzato M, Wolf L (2014) DeepFace: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, pp 1701\u20131708. \n                    https:\/\/doi.org\/10.1109\/CVPR.2014.220","DOI":"10.1109\/CVPR.2014.220"},{"key":"341_CR12","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver D, Huang A, Maddison CJ et al (2016) Mastering the game of Go with deep neural networks and tree search. Nature 529:484\u2013489. \n                    https:\/\/doi.org\/10.1038\/nature16961","journal-title":"Nature"},{"key":"341_CR13","unstructured":"Hadjeres G, Pachet F, Nielsen F (2016) DeepBach: a steerable model for bach chorales generation. \n                    arXiv:1612.01010"},{"key":"341_CR14","doi-asserted-by":"publisher","DOI":"10.1002\/joe.20070","author":"S Garg","year":"2017","unstructured":"Garg S, Rish I, Cecchi G, Lozano A (2017) Neurogenesis-inspired dictionary learning: online model adaption in a changing world. IJCAI Int Jt Conf Artif Intell. \n                    https:\/\/doi.org\/10.1002\/joe.20070","journal-title":"IJCAI Int Jt Conf Artif Intell"},{"key":"341_CR15","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1162\/tacl_a_00065","volume":"5","author":"M Johnson","year":"2016","unstructured":"Johnson M, Schuster M, Le QV et al (2016) Google\u2019s multilingual neural machine translation system: enabling zero-shot translation. Trans Assoc Comput Linguist 5:339","journal-title":"Trans Assoc Comput Linguist"},{"key":"341_CR16","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1016\/j.drudis.2018.01.039","volume":"23","author":"H Chen","year":"2018","unstructured":"Chen H, Engkvist O, Wang Y et al (2018) The rise of deep learning in drug discovery. Drug Discov Today 23:1241\u20131250. \n                    https:\/\/doi.org\/10.1016\/j.drudis.2018.01.039","journal-title":"Drug Discov Today"},{"key":"341_CR17","doi-asserted-by":"publisher","first-page":"1700123","DOI":"10.1002\/minf.201700123","volume":"37","author":"T Blaschke","year":"2018","unstructured":"Blaschke T, Olivecrona M, Engkvist O et al (2018) Application of generative autoencoder in de novo molecular design. Mol Inform 37:1700123. \n                    https:\/\/doi.org\/10.1002\/minf.201700123","journal-title":"Mol Inform"},{"key":"341_CR18","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s13321-017-0235-x","volume":"9","author":"M Olivecrona","year":"2017","unstructured":"Olivecrona M, Blaschke T, Engkvist O, Chen H (2017) Molecular de-novo design through deep reinforcement learning. J Cheminform 9:48. \n                    https:\/\/doi.org\/10.1186\/s13321-017-0235-x","journal-title":"J Cheminform"},{"key":"341_CR19","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1021\/acscentsci.7b00512","volume":"4","author":"MHS Segler","year":"2018","unstructured":"Segler MHS, Kogej T, Tyrchan C, Waller MP (2018) Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent Sci 4:120\u2013131. \n                    https:\/\/doi.org\/10.1021\/acscentsci.7b00512","journal-title":"ACS Cent Sci"},{"key":"341_CR20","doi-asserted-by":"publisher","DOI":"10.26434\/chemrxiv.5309668.v3","author":"B Sanchez-Lengeling","year":"2017","unstructured":"Sanchez-Lengeling B, Outeiral C, Guimaraes GL, Aspuru-Guzik A (2017) Optimizing distributions over molecular space. An objective-reinforced generative adversarial network for inverse-design chemistry (ORGANIC). ChemRxiv. \n                    https:\/\/doi.org\/10.26434\/chemrxiv.5309668.v3","journal-title":"ChemRxiv"},{"key":"341_CR21","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1021\/ci00057a005","volume":"28","author":"D Weininger","year":"1988","unstructured":"Weininger D (1988) SMILES, a chemical language and information system: 1: introduction to methodology and encoding rules. J Chem Inf Comput Sci 28:31\u201336. \n                    https:\/\/doi.org\/10.1021\/ci00057a005","journal-title":"J Chem Inf Comput Sci"},{"key":"341_CR22","doi-asserted-by":"publisher","first-page":"1736","DOI":"10.1021\/acs.jcim.8b00234","volume":"58","author":"K Preuer","year":"2018","unstructured":"Preuer K, Renz P, Unterthiner T et al (2018) Fr\u00e9chet ChemNet distance: a metric for generative models for molecules in drug discovery. J Chem Inf Model 58:1736\u20131741. \n                    https:\/\/doi.org\/10.1021\/acs.jcim.8b00234","journal-title":"J Chem Inf Model"},{"key":"341_CR23","doi-asserted-by":"publisher","first-page":"D1202","DOI":"10.1093\/nar\/gkv951","volume":"44","author":"S Kim","year":"2016","unstructured":"Kim S, Thiessen PA, Bolton EE et al (2016) PubChem substance and compound databases. Nucleic Acids Res 44:D1202\u2013D1213. \n                    https:\/\/doi.org\/10.1093\/nar\/gkv951","journal-title":"Nucleic Acids Res"},{"key":"341_CR24","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1021\/ci3001277","volume":"52","author":"JJ Irwin","year":"2012","unstructured":"Irwin JJ, Sterling T, Mysinger MM et al (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52:1757\u20131768. \n                    https:\/\/doi.org\/10.1021\/ci3001277","journal-title":"J Chem Inf Model"},{"key":"341_CR25","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85\u2013117. \n                    https:\/\/doi.org\/10.1016\/j.neunet.2014.09.003","journal-title":"Neural Netw"},{"key":"341_CR26","doi-asserted-by":"publisher","first-page":"2554","DOI":"10.1073\/pnas.79.8.2554","volume":"79","author":"JJ Hopfield","year":"1982","unstructured":"Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79:2554\u20132558. \n                    https:\/\/doi.org\/10.1073\/pnas.79.8.2554","journal-title":"Proc Natl Acad Sci"},{"key":"341_CR27","volume-title":"A field guide to dynamical recurrent networks","author":"S Hochreiter","year":"2009","unstructured":"Hochreiter S, Bengio Y, Frasconi P, Schmidhuber J (2009) Gradient flow in recurrent nets: the difficulty of learning longterm dependencies. In: Kremer SC, Kolen JF (eds) A field guide to dynamical recurrent networks. IEEE Press, London"},{"key":"341_CR28","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735\u20131780. \n                    https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"341_CR29","doi-asserted-by":"publisher","unstructured":"Cho K, van Merrienboer B, Gulcehre C et al (2014) Learning phrase representations using RNN encoder\u2013decoder for statistical machine translation. \n                    https:\/\/doi.org\/10.3115\/v1\/D14-1179","DOI":"10.3115\/v1\/D14-1179"},{"key":"341_CR30","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1109\/ISACC.2015.7377323","volume":"2015","author":"N Rahman","year":"2015","unstructured":"Rahman N, Borah B (2015) A survey on existing extractive techniques for query-based text summarization. Int Symp Adv Comput Commun ISACC 2015:98\u2013102. \n                    https:\/\/doi.org\/10.1109\/ISACC.2015.7377323","journal-title":"Int Symp Adv Comput Commun ISACC"},{"key":"341_CR31","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1162\/neco.1989.1.2.270","volume":"1","author":"RJ Williams","year":"1989","unstructured":"Williams RJ, Zipser D (1989) A learning algorithm for continually running fully recurrent neural networks. Neural Comput 1:270\u2013280. \n                    https:\/\/doi.org\/10.1162\/neco.1989.1.2.270","journal-title":"Neural Comput"},{"key":"341_CR32","unstructured":"Laplace P-S (1814) Chapitre II: De la probabilit\u00e9 des \u00e9v\u00e9nements compos\u00e9s d\u2019\u00e9v\u00e9nements simples dont les possibilit\u00e9s respectives sont donn\u00e9es (4). In: Th\u00e9orie analytique des probabilit\u00e9s, 2nd ed. Mme. Ve. Courcier, Paris, pp 191\u2013201"},{"key":"341_CR33","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1007\/s10822-011-9436-y","volume":"25","author":"LC Blum","year":"2011","unstructured":"Blum LC, Van Deursen R, Reymond JL (2011) Visualisation and subsets of the chemical universe database GDB-13 for virtual screening. J Comput Aided Mol Des 25:637\u2013647. \n                    https:\/\/doi.org\/10.1007\/s10822-011-9436-y","journal-title":"J Comput Aided Mol Des"},{"key":"341_CR34","doi-asserted-by":"publisher","first-page":"1803","DOI":"10.1002\/cmdc.200900317","volume":"4","author":"KT Nguyen","year":"2009","unstructured":"Nguyen KT, Blum LC, van Deursen R, Reymond J-L (2009) Classification of organic molecules by molecular quantum numbers. ChemMedChem 4:1803\u20131805. \n                    https:\/\/doi.org\/10.1002\/cmdc.200900317","journal-title":"ChemMedChem"},{"key":"341_CR35","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1021\/ci600423u","volume":"47","author":"T Fink","year":"2007","unstructured":"Fink T, Raymond JL (2007) Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: Assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discove. J Chem Inf Model 47:342\u2013353. \n                    https:\/\/doi.org\/10.1021\/ci600423u","journal-title":"J Chem Inf Model"},{"key":"341_CR36","doi-asserted-by":"publisher","unstructured":"Swain M, JoshuaMeyers (2018) mcs07\/MolVS: MolVS v0.1.1. \n                    https:\/\/doi.org\/10.5281\/zenodo.1217118","DOI":"10.5281\/zenodo.1217118"},{"key":"341_CR37","doi-asserted-by":"publisher","unstructured":"Landrum G, Kelley B, Tosco P, et al. (2018) rdkit\/rdkit: 2018_03_4 (Q1 2018) Release. \n                    https:\/\/doi.org\/10.5281\/zenodo.1345120","DOI":"10.5281\/zenodo.1345120"},{"key":"341_CR38","first-page":"1","volume":"30","author":"A Paszke","year":"2017","unstructured":"Paszke A, Chanan G, Lin Z et al (2017) Automatic differentiation in PyTorch. Adv Neural Inf Process Syst 30:1\u20134","journal-title":"Adv Neural Inf Process Syst"},{"key":"341_CR39","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1109\/ICCE.2017.7889386","volume":"2017","author":"D Lee","year":"2017","unstructured":"Lee D, Myung K (2017) Read my lips, login to the virtual world. IEEE Int Conf Consum Electron ICCE 2017:434\u2013435. \n                    https:\/\/doi.org\/10.1109\/ICCE.2017.7889386","journal-title":"IEEE Int Conf Consum Electron ICCE"},{"key":"341_CR40","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1145\/2934664","volume":"59","author":"M Zaharia","year":"2016","unstructured":"Zaharia M, Franklin MJ, Ghodsi A et al (2016) Apache spark. Commun ACM 59:56\u201365. \n                    https:\/\/doi.org\/10.1145\/2934664","journal-title":"Commun ACM"},{"key":"341_CR41","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1109\/MCSE.2007.55","volume":"9","author":"JD Hunter","year":"2007","unstructured":"Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Sci Eng 9:99\u2013104. \n                    https:\/\/doi.org\/10.1109\/MCSE.2007.55","journal-title":"Comput Sci Eng"},{"key":"341_CR42","doi-asserted-by":"publisher","unstructured":"Waskom M, Botvinnik O, O\u2019Kane D et al. (2018) mwaskom\/seaborn: v0.9.0 (July 2018). \n                    https:\/\/doi.org\/10.5281\/zenodo.1313201","DOI":"10.5281\/zenodo.1313201"},{"key":"341_CR43","doi-asserted-by":"publisher","unstructured":"Virtanen P, Gommers R, Burovski E et al. (2018) scipy\/scipy: SciPy 1.1.0. \n                    https:\/\/doi.org\/10.5281\/zenodo.1241501","DOI":"10.5281\/zenodo.1241501"},{"key":"341_CR44","doi-asserted-by":"publisher","DOI":"10.26434\/chemrxiv.7097960.v1","author":"N O\u2019Boyle","year":"2018","unstructured":"O\u2019Boyle N, Dalke A et al (2018) DeepSMILES: an adaptation of SMILES for use in machine-learning of chemical structures. chemRxiv. \n                    https:\/\/doi.org\/10.26434\/chemrxiv.7097960.v1","journal-title":"chemRxiv"},{"key":"341_CR45","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-statistics-010814-020120","author":"Y Li","year":"2018","unstructured":"Li Y, Vinyals O, Dyer C et al (2018) Learning deep generative models of graphs. ICLR. \n                    https:\/\/doi.org\/10.1146\/annurev-statistics-010814-020120","journal-title":"ICLR"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-019-0341-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13321-019-0341-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-019-0341-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T20:05:47Z","timestamp":1583870747000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-019-0341-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,12]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["341"],"URL":"https:\/\/doi.org\/10.1186\/s13321-019-0341-z","relation":{"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv.7172849.v1","asserted-by":"object"},{"id-type":"doi","id":"10.26434\/chemrxiv.7172849.v2","asserted-by":"object"}]},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,3,12]]},"assertion":[{"value":"19 October 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"20"}}