{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T04:32:17Z","timestamp":1754109137619,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":18,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T00:00:00Z","timestamp":1564012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["CCF-1533768","IIS-1838042","IIS-1418511"],"award-info":[{"award-number":["CCF-1533768","IIS-1838042","IIS-1418511"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,25]]},"DOI":"10.1145\/3292500.3332273","type":"proceedings-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T13:17:26Z","timestamp":1564147046000},"page":"3195-3196","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Tutorial"],"prefix":"10.1145","author":[{"given":"Cao","family":"Xiao","sequence":"first","affiliation":[{"name":"IQVIA, CAMBRIDGE, MA, USA"}]},{"given":"Jimeng","family":"Sun","sequence":"additional","affiliation":[{"name":"Georgia Tech, Atlanta, GA, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,7,25]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.molpharmaceut.6b00248"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.6b00367"},{"volume-title":"Lars Juhl Jensen, and Peer Bork","year":"2008","author":"Campillos Monica","key":"e_1_3_2_1_3_1"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2018.01.039"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.6b00601"},{"volume-title":"Advances in Neural Information Processing Systems 28","author":"Duvenaud David K","key":"e_1_3_2_1_6_1"},{"volume-title":"Pande","year":"2017","author":"Gomes Joseph","key":"e_1_3_2_1_7_1"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Bo Jin Haoyu Yang Cao Xiao Ping Zhang Xiaopeng Wei and Fei Wang. 2017. Multitask Dyadic Prediction and Its Application in Prediction of Adverse Drug- Drug Interaction. (2017).  Bo Jin Haoyu Yang Cao Xiao Ping Zhang Xiaopeng Wei and Fei Wang. 2017. Multitask Dyadic Prediction and Its Application in Prediction of Adverse Drug- Drug Interaction. (2017).","DOI":"10.1609\/aaai.v31i1.10718"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.molpharmaceut.7b00346"},{"volume-title":"Kipf and Max Welling","year":"2016","author":"Thomas","key":"e_1_3_2_1_10_1"},{"key":"e_1_3_2_1_11_1","unstructured":"Matt J. Kusner Brooks Paige and Jos\u00e9 Miguel Hern\u00e1ndez-Lobato. 2017. Grammar Variational Autoencoder. In ICML.   Matt J. Kusner Brooks Paige and Jos\u00e9 Miguel Hern\u00e1ndez-Lobato. 2017. Grammar Variational Autoencoder. In ICML."},{"key":"e_1_3_2_1_12_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 31 S. Bengio H. Wallach H. Larochelle K. Grauman N. Cesa-Bianchi and R. Garnett (Eds.). 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 31 S. Bengio H. Wallach H. Larochelle K. Grauman N. Cesa-Bianchi and R. Garnett (Eds.). 7113--7124."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.5555\/3304222.3304251"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Ioakeim Perros Fei Wang Ping Zhang Peter Walker Richard Vuduc Jyotishman Pathak and Jimeng Sun. {n. d.}. Polyadic Regression and its Application to Chemogenomics. 72--80. arXiv:https:\/\/epubs.siam.org\/doi\/pdf\/10.1137\/1.9781611974973.9  Ioakeim Perros Fei Wang Ping Zhang Peter Walker Richard Vuduc Jyotishman Pathak and Jimeng Sun. {n. d.}. Polyadic Regression and its Application to Chemogenomics. 72--80. arXiv:https:\/\/epubs.siam.org\/doi\/pdf\/10.1137\/1.9781611974973.9","DOI":"10.1137\/1.9781611974973.9"},{"volume-title":"Predicting serious rare adverse reactions of novel chemicals. Bioinformatics 34, 16 (03","year":"2018","author":"Poleksic Aleksandar","key":"e_1_3_2_1_15_1"},{"volume-title":"AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery. CoRR abs\/1510.02855","year":"2015","author":"Wallach Izhar","key":"e_1_3_2_1_16_1"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Cao Xiao Ping Zhang W. Chaovalitwongse Jianying Hu and Fei Wang. 2017. Adverse Drug Reaction Prediction with Symbolic Latent Dirichlet Allocation. (2017).  Cao Xiao Ping Zhang W. Chaovalitwongse Jianying Hu and Fei Wang. 2017. Adverse Drug Reaction Prediction with Symbolic Latent Dirichlet Allocation. (2017).","DOI":"10.1609\/aaai.v31i1.10717"},{"volume-title":"Advances in Neural Information Processing Systems 31","author":"You Jiaxuan","key":"e_1_3_2_1_18_1"}],"event":{"name":"KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Anchorage AK USA","acronym":"KDD '19"},"container-title":["Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3332273","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3332273","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3332273","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:25:56Z","timestamp":1750206356000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3332273"}},"subtitle":["Data Mining Methods for Drug Discovery and Development"],"short-title":[],"issued":{"date-parts":[[2019,7,25]]},"references-count":18,"alternative-id":["10.1145\/3292500.3332273","10.1145\/3292500"],"URL":"https:\/\/doi.org\/10.1145\/3292500.3332273","relation":{},"subject":[],"published":{"date-parts":[[2019,7,25]]},"assertion":[{"value":"2019-07-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}