{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T07:52:21Z","timestamp":1778226741847,"version":"3.51.4"},"reference-count":58,"publisher":"American Chemical Society (ACS)","issue":"3","license":[{"start":{"date-parts":[[2020,3,4]],"date-time":"2020-03-04T00:00:00Z","timestamp":1583280000000},"content-version":"vor","delay-in-days":58,"URL":"http:\/\/pubs.acs.org\/page\/policy\/authorchoice_termsofuse.html"}],"funder":[{"DOI":"10.13039\/501100003006","name":"Eidgen?ssische Technische Hochschule Z?rich","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003006","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001711","name":"Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung","doi-asserted-by":"publisher","award":["205321_182176"],"award-info":[{"award-number":["205321_182176"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Novartis Forschungsstiftung"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Chem. Inf. Model."],"published-print":{"date-parts":[[2020,3,23]]},"DOI":"10.1021\/acs.jcim.9b00943","type":"journal-article","created":{"date-parts":[[2020,1,6]],"date-time":"2020-01-06T17:06:40Z","timestamp":1578330400000},"page":"1175-1183","source":"Crossref","is-referenced-by-count":201,"title":["Bidirectional Molecule Generation with Recurrent Neural Networks"],"prefix":"10.1021","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8552-6615","authenticated-orcid":true,"given":"Francesca","family":"Grisoni","sequence":"first","affiliation":[{"name":"Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Moret","sequence":"additional","affiliation":[{"name":"Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robin","family":"Lingwood","sequence":"additional","affiliation":[{"name":"Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6706-1084","authenticated-orcid":true,"given":"Gisbert","family":"Schneider","sequence":"additional","affiliation":[{"name":"Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"316","published-online":{"date-parts":[[2020,1,6]]},"reference":[{"key":"ref1\/cit1","doi-asserted-by":"publisher","DOI":"10.1038\/nature03192"},{"key":"ref2\/cit2","doi-asserted-by":"publisher","DOI":"10.1016\/s1359-6446(97)01163-x"},{"key":"ref3\/cit3","doi-asserted-by":"publisher","DOI":"10.1021\/ci980083r"},{"key":"ref4\/cit4","doi-asserted-by":"publisher","DOI":"10.1021\/ci600423u"},{"key":"ref5\/cit5","doi-asserted-by":"publisher","DOI":"10.1021\/ci9502663"},{"key":"ref6\/cit6","doi-asserted-by":"publisher","DOI":"10.1002\/minf.200900038"},{"key":"ref7\/cit7","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.5b00628"},{"key":"ref8\/cit8","doi-asserted-by":"publisher","DOI":"10.1002\/minf.201400072"},{"key":"ref9\/cit9","doi-asserted-by":"publisher","DOI":"10.1021\/ci6005307"},{"key":"ref10\/cit10","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2014.09.042"},{"key":"ref11\/cit11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1002380"},{"key":"ref12\/cit12","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0067-7"},{"key":"ref13\/cit13","doi-asserted-by":"crossref","unstructured":"Rumelhart, D. E.; Hinton, G. E.; Williams, R. J. Learning Internal Representations by Error Propagation, ICS-8506; California University San Diego La Jolla, Institute for Cognitive Science, 1985.","DOI":"10.21236\/ADA164453"},{"key":"ref14\/cit14","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.79.8.2554"},{"key":"ref15\/cit15","unstructured":"Makhzani, A.; Shlens, J.; Jaitly, N.; Goodfellow, I.; Frey, B. Adversarial Autoencoders. 2015, arXiv:1511.05644. arXiv preprint."},{"key":"ref16\/cit16","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00572"},{"key":"ref17\/cit17","doi-asserted-by":"publisher","DOI":"10.1002\/cmdc.201800204"},{"key":"ref18\/cit18","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.7b00690"},{"key":"ref19\/cit19","doi-asserted-by":"publisher","DOI":"10.1021\/acs.molpharmaceut.8b00839"},{"key":"ref20\/cit20","unstructured":"Guimaraes, G. L.; Sanchez-Lengeling, B.; Outeiral, C.; Farias, P. L. C.; Aspuru-Guzik, A. Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models. 2017, arXiv:1705.10843. Cs Stat."},{"key":"ref21\/cit21","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00512"},{"key":"ref22\/cit22","doi-asserted-by":"publisher","DOI":"10.1021\/ci00057a005"},{"key":"ref23\/cit23","unstructured":"Ertl, P.; Lewis, R.; Martin, E.; Polyakov, V. In Silico Generation of Novel, Drug-like Chemical Matter Using the LSTM Neural Network. 2017, arXiv:1712.07449. Cs Q-Bio."},{"key":"ref24\/cit24","doi-asserted-by":"publisher","DOI":"10.1002\/minf.201700111"},{"key":"ref25\/cit25","unstructured":"Bjerrum, E. J.; Threlfall, R. Molecular Generation with Recurrent Neural Networks (RNNs). 2017, arXiv:1705.04612. Cs Q-Bio."},{"key":"ref26\/cit26","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.6b00754"},{"key":"ref27\/cit27","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00839"},{"key":"ref28\/cit28","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref29\/cit29","doi-asserted-by":"publisher","DOI":"10.1002\/minf.201700153"},{"key":"ref30\/cit30","doi-asserted-by":"publisher","DOI":"10.1038\/s42004-018-0068-1"},{"key":"ref31\/cit31","unstructured":"Mou, L.; Yan, R.; Li, G.; Zhang, L.; Jin, Z. Backward and Forward Language Modeling for Constrained Sentence Generation. 2015, arXiv:1512.06612. Cs."},{"key":"ref32\/cit32","unstructured":"Berglund, M.; Raiko, T.; Honkala, M.; K\u00e4rkk\u00e4inen, L.; Vetek, A.; Karhunen, J. T. Bidirectional Recurrent Neural Networks as Generative Models. In  Advances in Neural Information Processing Systems 28; Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R., Eds. Curran Associates, Inc., 2015; pp 856\u2013864."},{"key":"ref33\/cit33","volume-title":"Recurrent Neural Networks: Design and Applications","author":"Jain L. C.","year":"1999","edition":"1"},{"key":"ref34\/cit34","doi-asserted-by":"publisher","DOI":"10.1142\/s0218488598000094"},{"key":"ref35\/cit35","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(88)90007-x"},{"key":"ref36\/cit36","unstructured":"Chung, J.; Gulcehre, C.; Cho, K.; Bengio, Y. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. 2014, arXiv:1412.3555. Cs."},{"key":"ref37\/cit37","doi-asserted-by":"publisher","DOI":"10.1109\/78.650093"},{"key":"ref38\/cit38","doi-asserted-by":"crossref","unstructured":"Graves, A.; Jaitly, N.; Mohamed, A. Hybrid Speech Recognition with Deep Bidirectional LSTM.  2013 IEEE Workshop on Automatic Speech Recognition and Understanding; IEEE: Olomouc, Czech Republic, 2013; pp 273\u2013278.","DOI":"10.1109\/ASRU.2013.6707742"},{"key":"ref39\/cit39","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00263"},{"key":"ref40\/cit40","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00751"},{"key":"ref41\/cit41","doi-asserted-by":"crossref","unstructured":"Wang, C.; Yang, H.; Bartz, C.; Meinel, C. Image Captioning with Deep Bidirectional LSTMs.  Proceedings of the 24th ACM International Conference on Multimedia; MM \u201916; ACM: New York, NY, USA, 2016; pp 988\u2013997.","DOI":"10.1145\/2964284.2964299"},{"key":"ref42\/cit42","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty937"},{"key":"ref43\/cit43","unstructured":"Goh, G. B.; Hodas, N. O.; Siegel, C.; Vishnu, A. SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties. 2017, arXiv:1712.02034. Cs Stat."},{"key":"ref44\/cit44","unstructured":"ChEMBL22, 2016. 10.6019\/CHEMBL.Database.22."},{"key":"ref45\/cit45","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.5b00543"},{"key":"ref46\/cit46","unstructured":"RDKit:\nOpen-Source Cheminformatics, 2019, http:\/\/www.rdkit.org."},{"key":"ref47\/cit47","unstructured":"Kingma, D. P.; Ba, J. Adam: A Method for Stochastic Optimization. 2014, arXiv:1412.6980. Cs."},{"key":"ref48\/cit48","doi-asserted-by":"crossref","unstructured":"Moret, M.; Friedrich, L.; Grisoni, F.; Merk, D.; Schneider, G. Generating Customized Compound Libraries for Drug Discovery with Machine Intelligence. 2019, ChemRxiv:10119299. https:\/\/doi.org\/10.26434\/chemrxiv.10119299.v1.","DOI":"10.26434\/chemrxiv.10119299"},{"key":"ref49\/cit49","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-019-0030-7"},{"key":"ref50\/cit50","doi-asserted-by":"publisher","DOI":"10.1002\/qsar.200610091"},{"key":"ref51\/cit51","doi-asserted-by":"publisher","DOI":"10.1021\/jm9602928"},{"key":"ref52\/cit52","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00234"},{"key":"ref53\/cit53","doi-asserted-by":"crossref","unstructured":"Goh, G. B.; Siegel, C.; Vishnu, A.; Hodas, N. Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction.  Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining; KDD \u201918; ACM: New York, NY, USA, 2018; pp 302\u2013310.","DOI":"10.1145\/3219819.3219838"},{"key":"ref54\/cit54","unstructured":"Simard, P.; Victorri, B.; LeCun, Y.; Denker, J. Tangent Prop-a Formalism for Specifying Selected Invariances in an Adaptive Network.  Advances in Neural Information Processing Systems; NIPS, 1992; pp 895\u2013903."},{"key":"ref55\/cit55","unstructured":"Bjerrum, E. J. SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules. 2017, arXiv:1703.07076. Cs."},{"key":"ref56\/cit56","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkt1068"},{"key":"ref57\/cit57","unstructured":"Polykovskiy, D.; Zhebrak, A.; Sanchez-Lengeling, B.; Golovanov, S.; Tatanov, O.; Belyaev, S.; Kurbanov, R.; Artamonov, A.; Aladinskiy, V.; Veselov, M.; Kadurin, A.; Johansson, S.; Chen, H.; Nikolenko, S.; Aspuru-Guzik, A.; Zhavoronkov, A. Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models. 2018, arXiv:1811.12823. Cs Stat."},{"key":"ref58\/cit58","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-019-0393-0"}],"container-title":["Journal of Chemical Information and Modeling"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.9b00943","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.9b00943","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T04:52:24Z","timestamp":1682484744000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/doi\/10.1021\/acs.jcim.9b00943"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,6]]},"references-count":58,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2020,3,23]]}},"alternative-id":["10.1021\/acs.jcim.9b00943"],"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.9b00943","relation":{},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,6]]}}}