{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T15:58:46Z","timestamp":1785859126592,"version":"3.56.0"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2017,9,4]],"date-time":"2017-09-04T00:00:00Z","timestamp":1504483200000},"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":[[2017,12]]},"DOI":"10.1186\/s13321-017-0235-x","type":"journal-article","created":{"date-parts":[[2017,9,4]],"date-time":"2017-09-04T12:18:24Z","timestamp":1504527504000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1042,"title":["Molecular de-novo design through deep reinforcement learning"],"prefix":"10.1186","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8177-2787","authenticated-orcid":false,"given":"Marcus","family":"Olivecrona","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Blaschke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ola","family":"Engkvist","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongming","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2017,9,4]]},"reference":[{"issue":"8","key":"235_CR1","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1038\/nrd1799","volume":"4","author":"G Schneider","year":"2005","unstructured":"Schneider G, Fechner U (2005) Computer-based de novo design of drug-like molecules. Nat Rev Drug Discov 4(8):649\u2013663. doi: 10.1038\/nrd1799","journal-title":"Nat Rev Drug Discov"},{"issue":"1","key":"235_CR2","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/BF00124387","volume":"6","author":"HJ B\u00f6hm","year":"1992","unstructured":"B\u00f6hm HJ (1992) The computer program ludi: a new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des 6(1):61\u201378. doi: 10.1007\/BF00124387","journal-title":"J Comput Aided Mol Des"},{"issue":"1","key":"235_CR3","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1021\/ci00017a027","volume":"34","author":"VJ Gillet","year":"1994","unstructured":"Gillet VJ, Newell W, Mata P, Myatt G, Sike S, Zsoldos Z, Johnson AP (1994) Sprout: recent developments in the de novo design of molecules. J Chem Inf Comput Sci 34(1):207\u2013217. doi: 10.1021\/ci00017a027","journal-title":"J Chem Inf Comput Sci"},{"issue":"1","key":"235_CR4","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1021\/ci300535x","volume":"53","author":"L Ruddigkeit","year":"2013","unstructured":"Ruddigkeit L, Blum LC, Reymond JL (2013) Visualization and virtual screening of the chemical universe database gdb-17. J Chem Inf Model 53(1):56\u201365. doi: 10.1021\/ci300535x","journal-title":"J Chem Inf Model"},{"key":"235_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1002380","volume":"8","author":"M Hartenfeller","year":"2012","unstructured":"Hartenfeller M, Zettl H, Walter M, Rupp M, Reisen F, Proschak E, Weggen S, Stark H, Schneider G (2012) Dogs: reaction-driven de novo design of bioactive compounds. PLOS Comput Biol 8:1\u201312. doi: 10.1371\/journal.pcbi.1002380","journal-title":"PLOS Comput Biol"},{"issue":"4","key":"235_CR6","doi-asserted-by":"publisher","first-page":"415","DOI":"10.4155\/fmc.11.8","volume":"3","author":"G Schneider","year":"2011","unstructured":"Schneider G, Geppert T, Hartenfeller M, Reisen F, Klenner A, Reutlinger M, H\u00e4hnke V, Hiss JA, Zettl H, Keppner S, Sp\u00e4nkuch B, Schneider P (2011) Reaction-driven de novo design, synthesis and testing of potential type II kinase inhibitors. Future Med Chem 3(4):415\u2013424. doi: 10.4155\/fmc.11.8","journal-title":"Future Med Chem"},{"issue":"7428","key":"235_CR7","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1038\/nature11691","volume":"492","author":"J Besnard","year":"2012","unstructured":"Besnard J, Ruda GF, Setola V, Abecassis K, Rodriguiz RM, Huang X-P, Norval S, Sassano MF, Shin AI, Webster LA, Simeons FRC, Stojanovski L, Prat A, Seidah NG, Constam DB, Bickerton GR, Read KD, Wetsel WC, Gilbert IH, Roth BL, Hopkins AL (2012) Automated design of ligands to polypharmacological profiles. Nature 492(7428):215\u2013220. doi: 10.1038\/nature11691","journal-title":"Nature"},{"issue":"2","key":"235_CR8","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1021\/acs.jcim.5b00628","volume":"56","author":"T Miyao","year":"2016","unstructured":"Miyao T, Kaneko H, Funatsu K (2016) Inverse qspr\/qsar analysis for chemical structure generation (from y to x). J Chem Inf Model 56(2):286\u2013299. doi: 10.1021\/acs.jcim.5b00628","journal-title":"J Chem Inf Model"},{"issue":"4","key":"235_CR9","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.jmgm.2003.10.002","volume":"22","author":"CJ Churchwell","year":"2004","unstructured":"Churchwell CJ, Rintoul MD, Martin S Jr, Visco DP, Kotu A, Larson RS, Sillerud LO, Brown DC, Faulon J-L (2004) The signature molecular descriptor: 3. Inverse-quantitative structure-activity relationship of icam-1 inhibitory peptides. J Mol Graph Model 22(4):263\u2013273. doi: 10.1016\/j.jmgm.2003.10.002","journal-title":"J Mol Graph Model"},{"key":"235_CR10","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1186\/1758-2946-1-4","volume":"1","author":"WW Wong","year":"2009","unstructured":"Wong WW, Burkowski FJ (2009) A constructive approach for discovering new drug leads: using a kernel methodology for the inverse-qsar problem. J Cheminform 1:44. doi: 10.1186\/1758-2946-1-4","journal-title":"J Cheminform"},{"key":"235_CR11","doi-asserted-by":"crossref","unstructured":"Mikolov T, Karafi\u00e1t M, Burget L, Cernock\u2018y J, Khudanpur S (2010) Recurrent neural network based language model. In: Kobayashi T, Hirose K, Nakamura S (eds) 11th annual conference of the international speech communication association (INTERSPEECH 2010), Makuhari, Chiba, Japan. ISCA, 26\u201330 Sept 2010","DOI":"10.21437\/Interspeech.2010-343"},{"key":"235_CR12","unstructured":"Eck D, Schmidhuber J (2002) A first look at music composition using lstm recurrent neural networks. Technical report, Istituto Dalle Molle Di Studi Sull Intelligenza Artificiale"},{"key":"235_CR13","unstructured":"Segler MHS, Kogej T, Tyrchan C, Waller MP (2017) Generating focussed molecule libraries for drug discovery with recurrent neural networks. arXiv:1701.01329"},{"key":"235_CR14","unstructured":"G\u00f3mez-Bombarelli R, Duvenaud DK, Hern\u00e1andez-Lobato JM, Aguilera-Iparraguirre J, Hirzel TD, Adams RP, Aspuru-Guzik A (2016) Automatic chemical design using a data-driven continuous representation of molecules. CoRR. arXiv:1610.02415"},{"key":"235_CR15","unstructured":"Yu L, Zhang W, Wang J, Yu Y (2016) Seqgan: sequence generative adversarial nets with policy gradient. CoRR. arXiv:1609.05473"},{"key":"235_CR16","volume-title":"Reinforcement learning: an introduction","author":"R Sutton","year":"1998","unstructured":"Sutton R, Barton A (1998) Reinforcement learning: an introduction, 1st edn. MIT Press, Cambridge","edition":"1"},{"key":"235_CR17","unstructured":"Jaques N, Gu S, Turner RE, Eck D (2016) Tuning recurrent neural networks with reinforcement learning. CoRR. arXiv:1611.02796"},{"issue":"6","key":"235_CR18","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1021\/cr60274a001","volume":"71","author":"A Leo","year":"1971","unstructured":"Leo A, Hansch C, Elkins D (1971) Partition coefficients and their uses. Chem Rev 71(6):525\u2013616. doi: 10.1021\/cr60274a001","journal-title":"Chem Rev"},{"key":"235_CR19","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/nchem.1243","volume":"4","author":"GR Bickerton","year":"2012","unstructured":"Bickerton GR, Paolini GV, Besnard J, Muresan S, Hopkins AL (2012) Quantifying the chemical beauty of drugs. Nat Chem 4:90\u201398. doi: 10.1038\/nchem.1243","journal-title":"Nat Chem"},{"key":"235_CR20","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, Chambers J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D, Al-Lazikani B, Overington JP (2012) Chembl: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 40:1100\u20131107. doi: 10.1093\/nar\/gkr777 Version 22","journal-title":"Nucleic Acids Res"},{"key":"235_CR21","unstructured":"Goodfellow IJ, Mirza M, Xiao D, Courville A, Bengio Y (2013) An empirical investigation of catastrophic forgetting in gradient-based neural networks. arXiv:1312.6211"},{"key":"235_CR22","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning, 1st edn. MIT Press, Cambridge","edition":"1"},{"issue":"8","key":"235_CR23","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(8):1735\u20131780. doi: 10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"235_CR24","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555"},{"key":"235_CR25","unstructured":"SMILES. http:\/\/www.daylight.com\/dayhtml\/doc\/theory\/theory.smiles.html . Accessed 7 Apr 2017"},{"issue":"2","key":"235_CR26","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1021\/ci00062a008","volume":"29","author":"D Weininger","year":"1989","unstructured":"Weininger D, Weininger A, Weininger JL (1989) Smiles. 2. Algorithm for generation of unique smiles notation. J Chem Inf Comput Sci 29(2):97\u2013101. doi: 10.1021\/ci00062a008","journal-title":"J Chem Inf Comput Sci"},{"key":"235_CR27","unstructured":"RDKit: open source cheminformatics. Version: 2016-09-3. http:\/\/www.rdkit.org\/"},{"key":"235_CR28","unstructured":"Kingma DP, Ba J: Adam (2014) A method for stochastic optimization. CoRR. arXiv:1412.6980"},{"key":"235_CR29","unstructured":"Tensorflow. Version: 1.0.1. http:\/\/www.tensorflow.org"},{"issue":"3","key":"235_CR30","first-page":"229","volume":"8","author":"RJ Williams","year":"1992","unstructured":"Williams RJ (1992) Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach Learn 8(3):229\u2013256","journal-title":"Mach Learn"},{"key":"235_CR31","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems 27 (NIPS 2014), Montreal, Quebec, Canada. NIPS foundation, 8\u201313 Dec 2014"},{"key":"235_CR32","unstructured":"Lima Guimaraes G, Sanchez-Lengeling B, Cunha Farias PL, Aspuru-Guzik A (2017) Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models. arXiv:1705.10843"},{"issue":"1","key":"235_CR33","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s13321-017-0203-5","volume":"9","author":"J Sun","year":"2017","unstructured":"Sun J, Jeliazkova N, Chupakin V, Golib-Dzib J-F, Engkvist O, Carlsson L, Wegner J, Ceulemans H, Georgiev I, Jeliazkov V, Kochev N, Ashby TJ, Chen H (2017) Excape-db: an integrated large scale dataset facilitating big data analysis in chemogenomics. J Cheminform 9(1):17. doi: 10.1186\/s13321-017-0203-5","journal-title":"J Cheminform"},{"issue":"4","key":"235_CR34","doi-asserted-by":"publisher","first-page":"783","DOI":"10.1021\/ci400084k","volume":"53","author":"RP Sheridan","year":"2013","unstructured":"Sheridan RP (2013) Time-split cross-validation as a method for estimating the goodness of prospective prediction. J Chem Inf Model 53(4):783\u2013790","journal-title":"J Chem Inf Model"},{"key":"235_CR35","unstructured":"Unterthiner T, Mayr A, Steijaert M, Wegner JK, Ceulemans H, Hochreiter S (2014) Deep learning as an opportunity in virtual screening. In: Deep learning and representation learning workshop. NIPS, pp 1058\u20131066"},{"key":"235_CR36","doi-asserted-by":"publisher","first-page":"80","DOI":"10.3389\/fenvs.2015.00080","volume":"3","author":"A Mayr","year":"2016","unstructured":"Mayr A, Klambauer G, Unterthiner T, Hochreiter S (2016) Deeptox: toxicity prediction using deep learning. Front Environ Sci 3:80","journal-title":"Front Environ Sci"},{"key":"235_CR37","first-page":"547","volume":"37","author":"P Jaccard","year":"1901","unstructured":"Jaccard P (1901) \u00c9tude comparative de la distribution florale dans une portion des Alpes et des Jura. Bull Soc Vaud Sci Nat 37:547\u2013579","journal-title":"Bull Soc Vaud Sci Nat"},{"issue":"5","key":"235_CR38","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50(5):742\u2013754. doi: 10.1021\/ci100050t","journal-title":"J Chem Inf Model"},{"issue":"4","key":"235_CR39","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1021\/ci9803381","volume":"39","author":"D Butina","year":"1999","unstructured":"Butina D (1999) Unsupervised data base clustering based on daylight\u2019s fingerprint and tanimoto similarity: a fast and automated way to cluster small and large data sets. J Chem Inf Comput Sci 39(4):747\u2013750. doi: 10.1021\/ci9803381","journal-title":"J Chem Inf Comput Sci"},{"key":"235_CR40","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830 Version 0.17","journal-title":"J Mach Learn Res"},{"issue":"7587","key":"235_CR41","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, Guez A, Sifre L, Van Den Driessche G, Schrittwieser J, Antonoglou I, Panneershelvam V, Lanctot M et al (2016) Mastering the game of go with deep neural networks and tree search. Nature 529(7587):484\u2013489","journal-title":"Nature"},{"issue":"2","key":"235_CR42","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1002\/minf.201200141","volume":"32","author":"M Reutlinger","year":"2013","unstructured":"Reutlinger M, Koch CP, Reker D, Todoroff N, Schneider P, Rodrigues T, Schneider G (2013) Chemically advanced template search (CATS) for scaffold-hopping and prospective target prediction for \u2019orphan\u2019 molecules. Mol Inform 32(2):133\u2013138","journal-title":"Mol Inform"},{"issue":"6","key":"235_CR43","doi-asserted-by":"publisher","first-page":"1514","DOI":"10.1021\/ci900092y","volume":"49","author":"S Senger","year":"2009","unstructured":"Senger S (2009) Using Tversky similarity searches for core hopping: finding the needles in the haystack. J Chem Inf Model 49(6):1514\u20131524","journal-title":"J Chem Inf Model"},{"key":"235_CR44","unstructured":"Zaremba W, Sutskever I, Vinyals O (2014) Recurrent neural network regularization. CoRR. arXiv:1409.2329"},{"key":"235_CR45","unstructured":"Wan L, Zeiler M, Zhang S, LeCun Y, Fergus R (2013) Regularization of neural networks using dropconnect. In: Proceedings of the 30th international conference on international conference on machine learning, Vol 28. ICML\u201913, pp 1058\u20131066"},{"key":"235_CR46","first-page":"1137","volume":"3","author":"Y Bengio","year":"2003","unstructured":"Bengio Y, Ducharme R, Vincent P, Janvin C (2003) A neural probabilistic language model. J Mach Learn Res 3:1137\u20131155","journal-title":"J Mach Learn Res"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-017-0235-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13321-017-0235-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-017-0235-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T04:46:37Z","timestamp":1659415597000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-017-0235-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,4]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,12]]}},"alternative-id":["235"],"URL":"https:\/\/doi.org\/10.1186\/s13321-017-0235-x","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,9,4]]},"assertion":[{"value":"14 May 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2017","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2017","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"48"}}