{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T11:32:44Z","timestamp":1787916764415,"version":"build-2784847793"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF IIS","award":["1716432 1750326"],"award-info":[{"award-number":["1716432 1750326"]}]},{"name":"ONR","award":["N00014-18-1-2585"],"award-info":[{"award-number":["N00014-18-1-2585"]}]},{"name":"Amazon Web Service (AWS) Machine Learning for Research Award"},{"name":"Google Faculty Research Award"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,8,23]]},"DOI":"10.1145\/3394486.3403104","type":"proceedings-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T19:03:55Z","timestamp":1597950235000},"page":"617-626","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":221,"title":["MoFlow: An Invertible Flow Model for Generating Molecular Graphs"],"prefix":"10.1145","author":[{"given":"Chengxi","family":"Zang","sequence":"first","affiliation":[{"name":"Weill Cornell Medicine, Cornell University, NYC, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Wang","sequence":"additional","affiliation":[{"name":"Weill Cornell Medicine, Cornell University, NYC, NY, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMp1500848"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-015-0069-3"},{"key":"e_1_3_2_2_3_1","volume-title":"Quantifying the chemical beauty of drugs. Nature chemistry","author":"Bickerton G Richard","year":"2012","unstructured":"G Richard Bickerton , Gaia V Paolini , J\u00e9r\u00e9my Besnard , Sorel Muresan , and Andrew L Hopkins . 2012. Quantifying the chemical beauty of drugs. Nature chemistry , Vol. 4 , 2 ( 2012 ), 90. G Richard Bickerton, Gaia V Paolini, J\u00e9r\u00e9my Besnard, Sorel Muresan, and Andrew L Hopkins. 2012. Quantifying the chemical beauty of drugs. Nature chemistry, Vol. 4, 2 (2012), 90."},{"key":"e_1_3_2_2_4_1","volume-title":"A Two-Step Graph Convolutional Decoder for Molecule Generation. arXiv preprint arXiv:1906.03412","author":"Bresson Xavier","year":"2019","unstructured":"Xavier Bresson and Thomas Laurent . 2019. A Two-Step Graph Convolutional Decoder for Molecule Generation. arXiv preprint arXiv:1906.03412 ( 2019 ). Xavier Bresson and Thomas Laurent. 2019. A Two-Step Graph Convolutional Decoder for Molecule Generation. arXiv preprint arXiv:1906.03412 (2019)."},{"key":"e_1_3_2_2_5_1","volume-title":"Syntax-directed variational autoencoder for structured data. arXiv preprint arXiv:1802.08786","author":"Dai Hanjun","year":"2018","unstructured":"Hanjun Dai , Yingtao Tian , Bo Dai , Steven Skiena , and Le Song . 2018. Syntax-directed variational autoencoder for structured data. arXiv preprint arXiv:1802.08786 ( 2018 ). Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, and Le Song. 2018. Syntax-directed variational autoencoder for structured data. arXiv preprint arXiv:1802.08786 (2018)."},{"key":"e_1_3_2_2_6_1","volume-title":"MolGAN: An implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973","author":"Cao Nicola De","year":"2018","unstructured":"Nicola De Cao and Thomas Kipf . 2018. MolGAN: An implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973 ( 2018 ). Nicola De Cao and Thomas Kipf. 2018. MolGAN: An implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973 (2018)."},{"key":"e_1_3_2_2_7_1","volume-title":"Nice: Non-linear independent components estimation. arXiv preprint arXiv:1410.8516","author":"Dinh Laurent","year":"2014","unstructured":"Laurent Dinh , David Krueger , and Yoshua Bengio . 2014 . Nice: Non-linear independent components estimation. arXiv preprint arXiv:1410.8516 (2014). Laurent Dinh, David Krueger, and Yoshua Bengio. 2014. Nice: Non-linear independent components estimation. arXiv preprint arXiv:1410.8516 (2014)."},{"key":"e_1_3_2_2_8_1","volume-title":"Density estimation using real nvp. arXiv preprint arXiv:1605.08803","author":"Dinh Laurent","year":"2016","unstructured":"Laurent Dinh , Jascha Sohl-Dickstein , and Samy Bengio . 2016. Density estimation using real nvp. arXiv preprint arXiv:1605.08803 ( 2016 ). Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. 2016. Density estimation using real nvp. arXiv preprint arXiv:1605.08803 (2016)."},{"key":"e_1_3_2_2_9_1","volume-title":"Benjam'in S\u00e1nchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Al\u00e1n Aspuru-Guzik.","author":"G\u00f3mez-Bombarelli Rafael","year":"2018","unstructured":"Rafael G\u00f3mez-Bombarelli , Jennifer N Wei , David Duvenaud , Jos\u00e9 Miguel Hern\u00e1ndez-Lobato , Benjam'in S\u00e1nchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Al\u00e1n Aspuru-Guzik. 2018 . Automatic chemical design using a data-driven continuous representation of molecules. ACS central science, Vol. 4 , 2 (2018), 268--276. Rafael G\u00f3mez-Bombarelli, Jennifer N Wei, David Duvenaud, Jos\u00e9 Miguel Hern\u00e1ndez-Lobato, Benjam'in S\u00e1nchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Al\u00e1n Aspuru-Guzik. 2018. Automatic chemical design using a data-driven continuous representation of molecules. ACS central science, Vol. 4, 2 (2018), 268--276."},{"key":"e_1_3_2_2_10_1","volume-title":"Graph residual flow for molecular graph generation. arXiv preprint arXiv:1909.13521","author":"Honda Shion","year":"2019","unstructured":"Shion Honda , Hirotaka Akita , Katsuhiko Ishiguro , Toshiki Nakanishi , and Kenta Oono . 2019. Graph residual flow for molecular graph generation. arXiv preprint arXiv:1909.13521 ( 2019 ). Shion Honda, Hirotaka Akita, Katsuhiko Ishiguro, Toshiki Nakanishi, and Kenta Oono. 2019. Graph residual flow for molecular graph generation. arXiv preprint arXiv:1909.13521 (2019)."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci3001277"},{"key":"e_1_3_2_2_12_1","volume-title":"Junction tree variational autoencoder for molecular graph generation. arXiv preprint arXiv:1802.04364","author":"Jin Wengong","year":"2018","unstructured":"Wengong Jin , Regina Barzilay , and Tommi Jaakkola . 2018. Junction tree variational autoencoder for molecular graph generation. arXiv preprint arXiv:1802.04364 ( 2018 ). Wengong Jin, Regina Barzilay, and Tommi Jaakkola. 2018. Junction tree variational autoencoder for molecular graph generation. arXiv preprint arXiv:1802.04364 (2018)."},{"key":"e_1_3_2_2_13_1","volume-title":"Glow: Generative flow with invertible 1x1 convolutions. In Advances in Neural Information Processing Systems. 10215--10224.","author":"Kingma Durk P","year":"2018","unstructured":"Durk P Kingma and Prafulla Dhariwal . 2018 . Glow: Generative flow with invertible 1x1 convolutions. In Advances in Neural Information Processing Systems. 10215--10224. Durk P Kingma and Prafulla Dhariwal. 2018. Glow: Generative flow with invertible 1x1 convolutions. In Advances in Neural Information Processing Systems. 10215--10224."},{"key":"e_1_3_2_2_14_1","volume-title":"Normalizing flows: Introduction and ideas. arXiv preprint arXiv:1908.09257","author":"Kobyzev Ivan","year":"2019","unstructured":"Ivan Kobyzev , Simon Prince , and Marcus A Brubaker . 2019. Normalizing flows: Introduction and ideas. arXiv preprint arXiv:1908.09257 ( 2019 ). Ivan Kobyzev, Simon Prince, and Marcus A Brubaker. 2019. Normalizing flows: Introduction and ideas. arXiv preprint arXiv:1908.09257 (2019)."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305381.3305582"},{"key":"e_1_3_2_2_16_1","unstructured":"Greg Landrum et al. 2006. RDKit: Open-source cheminformatics.  Greg Landrum et al. 2006. RDKit: Open-source cheminformatics."},{"key":"e_1_3_2_2_17_1","unstructured":"Jenny Liu Aviral Kumar Jimmy Ba Jamie Kiros and Kevin Swersky. 2019. Graph normalizing flows. In Advances in Neural Information Processing Systems. 13556--13566.  Jenny Liu Aviral Kumar Jimmy Ba Jamie Kiros and Kevin Swersky. 2019. Graph normalizing flows. In Advances in Neural Information Processing Systems. 13556--13566."},{"key":"e_1_3_2_2_18_1","unstructured":"Qi Liu Miltiadis Allamanis Marc Brockschmidt and Alexander Gaunt. 2018. Constrained graph variational autoencoders for molecule design. In Advances in Neural Information Processing Systems. 7795--7804.  Qi Liu Miltiadis Allamanis Marc Brockschmidt and Alexander Gaunt. 2018. Constrained graph variational autoencoders for molecule design. In Advances in Neural Information Processing Systems. 7795--7804."},{"key":"e_1_3_2_2_19_1","unstructured":"Tengfei Ma Jie Chen and Cao Xiao. 2018. Constrained generation of semantically valid graphs via regularizing variational autoencoders. In Advances in Neural Information Processing Systems. 7113--7124.  Tengfei Ma Jie Chen and Cao Xiao. 2018. Constrained generation of semantically valid graphs via regularizing variational autoencoders. In Advances in Neural Information Processing Systems. 7113--7124."},{"key":"e_1_3_2_2_20_1","volume-title":"GraphNVP: An Invertible Flow Model for Generating Molecular Graphs. arXiv preprint arXiv:1905.11600","author":"Madhawa Kaushalya","year":"2019","unstructured":"Kaushalya Madhawa , Katushiko Ishiguro , Kosuke Nakago , and Motoki Abe . 2019. GraphNVP: An Invertible Flow Model for Generating Molecular Graphs. arXiv preprint arXiv:1905.11600 ( 2019 ). Kaushalya Madhawa, Katushiko Ishiguro, Kosuke Nakago, and Motoki Abe. 2019. GraphNVP: An Invertible Flow Model for Generating Molecular Graphs. arXiv preprint arXiv:1905.11600 (2019)."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/549445a"},{"key":"e_1_3_2_2_22_1","volume-title":"Shakir Mohamed, and Balaji Lakshminarayanan.","author":"Papamakarios George","year":"2019","unstructured":"George Papamakarios , Eric Nalisnick , Danilo Jimenez Rezende , Shakir Mohamed, and Balaji Lakshminarayanan. 2019 . Normalizing Flows for Probabilistic Modeling and Inference . arXiv preprint arXiv:1912.02762 (2019). George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan. 2019. Normalizing Flows for Probabilistic Modeling and Inference. arXiv preprint arXiv:1912.02762 (2019)."},{"key":"e_1_3_2_2_23_1","volume-title":"Image transformer. arXiv preprint arXiv:1802.05751","author":"Parmar Niki","year":"2018","unstructured":"Niki Parmar , Ashish Vaswani , Jakob Uszkoreit , \u0141ukasz Kaiser , Noam Shazeer , Alexander Ku , and Dustin Tran . 2018. Image transformer. arXiv preprint arXiv:1802.05751 ( 2018 ). Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, \u0141ukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran. 2018. Image transformer. arXiv preprint arXiv:1802.05751 (2018)."},{"key":"e_1_3_2_2_24_1","volume-title":"How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature reviews Drug discovery","author":"Paul Steven M","year":"2010","unstructured":"Steven M Paul , Daniel S Mytelka , Christopher T Dunwiddie , Charles C Persinger , Bernard H Munos , Stacy R Lindborg , and Aaron L Schacht . 2010. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature reviews Drug discovery , Vol. 9 , 3 ( 2010 ), 203. Steven M Paul, Daniel S Mytelka, Christopher T Dunwiddie, Charles C Persinger, Bernard H Munos, Stacy R Lindborg, and Aaron L Schacht. 2010. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature reviews Drug discovery, Vol. 9, 3 (2010), 203."},{"key":"e_1_3_2_2_25_1","volume-title":"MolecularRNN: Generating realistic molecular graphs with optimized properties. arXiv preprint arXiv:1905.13372","author":"Popova Mariya","year":"2019","unstructured":"Mariya Popova , Mykhailo Shvets , Junier Oliva , and Olexandr Isayev . 2019. MolecularRNN: Generating realistic molecular graphs with optimized properties. arXiv preprint arXiv:1905.13372 ( 2019 ). Mariya Popova, Mykhailo Shvets, Junier Oliva, and Olexandr Isayev. 2019. MolecularRNN: Generating realistic molecular graphs with optimized properties. arXiv preprint arXiv:1905.13372 (2019)."},{"key":"e_1_3_2_2_26_1","volume-title":"Quantum chemistry structures and properties of 134 kilo molecules. Scientific data","author":"Ramakrishnan Raghunathan","year":"2014","unstructured":"Raghunathan Ramakrishnan , Pavlo O Dral , Matthias Rupp , and O Anatole Von Lilienfeld . 2014. Quantum chemistry structures and properties of 134 kilo molecules. Scientific data , Vol. 1 ( 2014 ), 140022. Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld. 2014. Quantum chemistry structures and properties of 134 kilo molecules. Scientific data, Vol. 1 (2014), 140022."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci100050t"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"e_1_3_2_2_29_1","volume-title":"ICLR 2020","author":"Shi Chence","year":"2020","unstructured":"Chence Shi , Minkai Xu , Zhaocheng Zhu , Weinan Zhang , Ming Zhang , and Jian. Tang. 2020 . GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation . ICLR 2020 , Addis Ababa, Ethiopia, Apr.26- Apr. 30, 2020 (2020). Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian. Tang. 2020. GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation. ICLR 2020, Addis Ababa, Ethiopia, Apr.26-Apr. 30, 2020 (2020)."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01418-6_41"},{"key":"e_1_3_2_2_31_1","volume-title":"Graph convolutional networks for computational drug development and discovery. Briefings in bioinformatics","author":"Sun Mengying","year":"2019","unstructured":"Mengying Sun , Sendong Zhao , Coryandar Gilvary , Olivier Elemento , Jiayu Zhou , and Fei Wang . 2019. Graph convolutional networks for computational drug development and discovery. Briefings in bioinformatics ( 2019 ). Mengying Sun, Sendong Zhao, Coryandar Gilvary, Olivier Elemento, Jiayu Zhou, and Fei Wang. 2019. Graph convolutional networks for computational drug development and discovery. Briefings in bioinformatics (2019)."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci00062a008"},{"key":"e_1_3_2_2_33_1","unstructured":"Jiaxuan You Bowen Liu Zhitao Ying Vijay Pande and Jure Leskovec. 2018. Graph convolutional policy network for goal-directed molecular graph generation. In Advances in Neural Information Processing Systems. 6410--6421.  Jiaxuan You Bowen Liu Zhitao Ying Vijay Pande and Jure Leskovec. 2018. Graph convolutional policy network for goal-directed molecular graph generation. In Advances in Neural Information Processing Systems. 6410--6421."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"crossref","unstructured":"Alex Zhavoronkov Yan A Ivanenkov Alex Aliper Mark S Veselov Vladimir A Aladinskiy Anastasiya V Aladinskaya Victor A Terentiev Daniil A Polykovskiy Maksim D Kuznetsov etal 2019. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature biotechnology Vol. 37 9 (2019) 1038--1040.  Alex Zhavoronkov Yan A Ivanenkov Alex Aliper Mark S Veselov Vladimir A Aladinskiy Anastasiya V Aladinskaya Victor A Terentiev Daniil A Polykovskiy Maksim D Kuznetsov et al. 2019. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature biotechnology Vol. 37 9 (2019) 1038--1040.","DOI":"10.1038\/s41587-019-0224-x"}],"event":{"name":"KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event CA USA","acronym":"KDD '20","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403104","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3403104","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3403104","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:31:34Z","timestamp":1750181494000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":34,"alternative-id":["10.1145\/3394486.3403104","10.1145\/3394486"],"URL":"https:\/\/doi.org\/10.1145\/3394486.3403104","relation":{},"subject":[],"published":{"date-parts":[[2020,8,20]]},"assertion":[{"value":"2020-08-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}