{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T08:35:53Z","timestamp":1771576553908,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":53,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"DARPA","award":["W911NF20102551"],"award-info":[{"award-number":["W911NF20102551"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539023","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:12Z","timestamp":1660331172000},"page":"3819-3828","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["ChemicalX: A Deep Learning Library for Drug Pair Scoring"],"prefix":"10.1145","author":[{"given":"Benedek","family":"Rozemberczki","sequence":"first","affiliation":[{"name":"AstraZeneca, Cambridge, United Kingdom"}]},{"given":"Charles Tapley","family":"Hoyt","sequence":"additional","affiliation":[{"name":"Harvard Medical School, Cambridge, MA, USA"}]},{"given":"Anna","family":"Gogleva","sequence":"additional","affiliation":[{"name":"AstraZeneca, Cambridge, United Kingdom"}]},{"given":"Piotr","family":"Grabowski","sequence":"additional","affiliation":[{"name":"AstraZeneca, Cambridge, United Kingdom"}]},{"given":"Klas","family":"Karis","sequence":"additional","affiliation":[{"name":"Harvard Medical School, Cambridge, MA, USA"}]},{"given":"Andrej","family":"Lamov","sequence":"additional","affiliation":[{"name":"AstraZeneca, Gothenburg, Sweden"}]},{"given":"Andriy","family":"Nikolov","sequence":"additional","affiliation":[{"name":"AstraZeneca, Cambridge, United Kingdom"}]},{"given":"Sebastian","family":"Nilsson","sequence":"additional","affiliation":[{"name":"AstraZeneca, Gothenburg, Sweden"}]},{"given":"Michael","family":"Ughetto","sequence":"additional","affiliation":[{"name":"AstraZeneca, Gothenburg, Sweden"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"Vanderbilt University, Nashville, TN, USA"}]},{"given":"Tyler","family":"Derr","sequence":"additional","affiliation":[{"name":"Vanderbilt University, Nashville, TN, USA"}]},{"given":"Benjamin M.","family":"Gyori","sequence":"additional","affiliation":[{"name":"Harvard Medical School, Cambridge, MA, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Tensorflow: A System for Large-Scale Machine Learning. In 12th (USENIX) symposium on operating systems design and implementation (OSDI 16)","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi, Paul Barham, Jianmin Chen, et al. 2016. Tensorflow: A System for Large-Scale Machine Learning. In 12th (USENIX) symposium on operating systems design and implementation (OSDI 16). 265--283."},{"key":"e_1_3_2_1_2_1","first-page":"1","article-title":"PyKEEN 1.0: a Python Library for Training and Evaluating Knowledge Graph Embeddings","volume":"22","author":"Ali Mehdi","year":"2021","unstructured":"Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Sahand Sharifzadeh, Volker Tresp, and Jens Lehmann. 2021. PyKEEN 1.0: a Python Library for Training and Evaluating Knowledge Graph Embeddings. Journal of Machine Learning Research 22, 82 (2021), 1--6.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_3_1","volume-title":"MatchMaker: A Deep Learning Framework for Drug Synergy Prediction","author":"Brahim Kuru Halil","year":"2021","unstructured":"Kuru Halil Brahim, Oznur Tastan, and Ercument Cicek. 2021. MatchMaker: A Deep Learning Framework for Drug Synergy Prediction. IEEE\/ACM Transactions on Computational Biology and Bioinformatics (2021)."},{"key":"e_1_3_2_1_4_1","volume-title":"DeepDrug: A General Graph- Based Deep Learning Framework for Drug Relation Prediction. bioRxiv","author":"Cao Xusheng","year":"2020","unstructured":"Xusheng Cao, Rui Fan, and Wanwen Zeng. 2020. DeepDrug: A General Graph- Based Deep Learning Framework for Drug Relation Prediction. bioRxiv (2020)."},{"key":"e_1_3_2_1_5_1","unstructured":"Yukuo Cen Zhenyu Hou Yan Wang Qibin Chen et al. 2021. CogDL: An Extensive Toolkit for Deep Learning on Graphs. (2021)."},{"key":"e_1_3_2_1_6_1","unstructured":"Tianqi Chen Mu Li Yutian Li Min Lin et al. 2015. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. arXiv preprint 1512.01274 (2015)."},{"key":"e_1_3_2_1_7_1","first-page":"47","article-title":"GCN-BMP","volume":"179","author":"Chen Xin","year":"2020","unstructured":"Xin Chen, Xien Liu, and Ji Wu. 2020. GCN-BMP: Investigating Graph Representation Learning for DDI Prediction Task. Methods 179 (2020), 47--54. Interpretable machine learning in bioinformatics.","journal-title":"Investigating Graph Representation Learning for DDI Prediction Task. Methods"},{"key":"e_1_3_2_1_8_1","unstructured":"CSIRO's Data61. 2018. StellarGraph Machine Learning Library. https:\/\/github.com\/stellargraph\/stellargraph."},{"key":"e_1_3_2_1_9_1","volume-title":"Drug-Drug Adverse Effect Prediction with Graph Co-Attention. ICML Workshop on Computational Biology","author":"Deac Andreea","year":"2019","unstructured":"Andreea Deac, Yu-Hsiang Huang, Petar Velickovic, Pietro Li\u00f2, and Jian Tang. 2019. Drug-Drug Adverse Effect Prediction with Graph Co-Attention. ICML Workshop on Computational Biology (2019)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci010132r"},{"key":"e_1_3_2_1_11_1","first-page":"2224","article-title":"Convolutional Networks on Graphs for Learning Molecular Fingerprints","volume":"28","author":"Duvenaud David K","year":"2015","unstructured":"David K Duvenaud, Dougal Maclaurin, et al. 2015. Convolutional Networks on Graphs for Learning Molecular Fingerprints. Advances in Neural Information Processing Systems 28 (2015), 2224--2232.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_12_1","volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.","author":"Fey Matthias","unstructured":"Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Thomas Gaudelet Ben Day Arian R Jamasb Jyothish Soman et al. 2021. Utilizing Graph Machine Learning within Drug Discovery and Development. Briefings in Bioinformatics 22 6 (2021).","DOI":"10.1093\/bib\/bbab159"},{"key":"e_1_3_2_1_14_1","volume-title":"Neural Message Passing for Quantum Chemistry. In International Conference on Machine Learning. 1263--1272","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel Schoenholz, Patrick Riley, Oriol Vinyals, and George Dahl. 2017. Neural Message Passing for Quantum Chemistry. In International Conference on Machine Learning. 1263--1272."},{"key":"e_1_3_2_1_15_1","volume-title":"Jraph: A Library for Graph Neural Networks in Jax.","author":"Godwin Jonathan","year":"2020","unstructured":"Jonathan Godwin, Thomas Keck, Peter Battaglia, Victor Bapst, Thomas Kipf, et al. 2020. Jraph: A Library for Graph Neural Networks in Jax."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00572"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2020.3039072"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-015-0068-4"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Jun Hu Shengsheng Qian Quan Fang et al. 2021. Efficient Graph Deep Learning in TensorFlow with TF Geometric. arXiv preprint 2101.11552 (2021).","DOI":"10.1145\/3474085.3478322"},{"key":"e_1_3_2_1_20_1","volume-title":"Strategies for Pre-training Graph Neural Networks. In International Conference on Learning Representations.","author":"Hu Weihua","year":"2019","unstructured":"Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. 2019. Strategies for Pre-training Graph Neural Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_21_1","volume-title":"Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development. In 35th Conference on Neural Information Processing Systems.","author":"Huang Kexin","year":"2021","unstructured":"Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, et al. 2021. Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development. In 35th Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_1_22_1","volume-title":"Lucas M Glass, and Jimeng Sun.","author":"Huang Kexin","year":"2020","unstructured":"Kexin Huang, Cao Xiao, Trong Nghia Hoang, Lucas M Glass, and Jimeng Sun. 2020. CASTER: Predicting Drug Interactions with Chemical Substructure Representation. AAAI (2020)."},{"key":"e_1_3_2_1_23_1","volume-title":"JAX: Composable Transformations of Python+NumPy Programs.","author":"Bradbury James","year":"2018","unstructured":"James Bradbury and Roy Frostig and Peter Hawkins and Matthew James Johnson and others. 2018. JAX: Composable Transformations of Python+NumPy Programs."},{"key":"e_1_3_2_1_24_1","volume-title":"Adam: A Method for Stochastic Optimization. In Internation Conference on Learning Representations.","author":"Kingma Diederik P","year":"2015","unstructured":"Diederik P Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In Internation Conference on Learning Representations."},{"key":"e_1_3_2_1_25_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations (ICLR).","author":"Thomas","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/aba947"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1021\/acsomega.1c04017"},{"key":"e_1_3_2_1_28_1","volume-title":"DrugCombDB: A Comprehensive Database of Drug Combinations Toward the Discovery of Combinatorial Therapy. Nucleic acids research 48","author":"Liu Hui","year":"2020","unstructured":"Hui Liu, Wenhao Zhang, Bo Zou, Jinxian Wang, and Yuanyuan Deng. 2020. DrugCombDB: A Comprehensive Database of Drug Combinations Toward the Discovery of Combinatorial Therapy. Nucleic acids research 48 (2020), 871--881."},{"key":"e_1_3_2_1_29_1","first-page":"1","article-title":"DIG: A Turnkey Library for Diving into Graph Deep Learning Research","volume":"22","author":"Liu Meng","year":"2021","unstructured":"Meng Liu, Youzhi Luo, Limei Wang, et al. 2021. DIG: A Turnkey Library for Diving into Graph Deep Learning Research. Journal of Machine Learning Research 22, 240 (2021), 1--9.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/abcf91"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1021\/c160017a018"},{"key":"e_1_3_2_1_32_1","volume-title":"Chainer Chemistry: A Library for Deep Learning in Biology and Chemistry. https:\/\/github.com\/chainer\/chainer-chemistry","author":"Motoki Abe","year":"2017","unstructured":"Abe Motoki, Mihai Mororiu, Tomoya Otabi, Kenshin Abe, and Others. 2017. Chainer Chemistry: A Library for Deep Learning in Biology and Chemistry. https:\/\/github.com\/chainer\/chainer-chemistry"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104425"},{"key":"e_1_3_2_1_34_1","volume-title":"SSI--DDI: Substructure-- Substructure Interactions for Drug--Drug Interaction Prediction. Briefings in Bioinformatics","author":"Nyamabo Arnold K","year":"2021","unstructured":"Arnold K Nyamabo, Hui Yu, and Jian-Yu Shi. 2021. SSI--DDI: Substructure-- Substructure Interactions for Drug--Drug Interaction Prediction. Briefings in Bioinformatics (2021)."},{"key":"e_1_3_2_1_35_1","volume-title":"DeepSMILES: An Adaptation of SMILES for Use in Machine-Learning of Chemical Structures. ChemRxiv","author":"O'Boyle Noel","year":"2018","unstructured":"Noel O'Boyle and Andrew Dalke. 2018. DeepSMILES: An Adaptation of SMILES for Use in Machine-Learning of Chemical Structures. ChemRxiv (2018)."},{"key":"e_1_3_2_1_36_1","first-page":"8026","article-title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","volume":"32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, et al. 2019. PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems 32 (2019), 8026--8037.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_37_1","first-page":"1538","article-title":"DeepSynergy","volume":"34","author":"Preuer Kristina","year":"2018","unstructured":"Kristina Preuer, Richard PI Lewis, Sepp Hochreiter, Andreas Bender, Krishna C Bulusu, and G\u00fcnter Klambauer. 2018. DeepSynergy: Predicting Anti-Cancer Drug Synergy with Deep Learning. Bioinformatics 34, 9 (2018), 1538--1546.","journal-title":"Predicting Anti-Cancer Drug Synergy with Deep Learning. Bioinformatics"},{"key":"e_1_3_2_1_38_1","unstructured":"Bharath Ramsundar Peter Eastman Patrick Walters Vijay Pande et al. 2019. Deep Learning for the Life Sciences. O'Reilly Media."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Benedek Rozemberczki Stephen Bonner Andriy Nikolov Michael Ughetto Sebastian Nilsson and Eliseo Papa. 2021. A Unified View of Relational Deep Learning for Drug Pair Scoring. arXiv:2111.02916 [cs.LG]","DOI":"10.24963\/ijcai.2022\/777"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482014"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1803294115"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i10.7236"},{"key":"e_1_3_2_1_44_1","volume-title":"Data-Driven Prediction of Drug Effects and Interactions. Science translational medicine 4, 125","author":"Tatonetti Nicholas P","year":"2012","unstructured":"Nicholas P Tatonetti, P Ye Patrick, Roxana Daneshjou, and Russ B Altman. 2012. Data-Driven Prediction of Drug Effects and Interactions. Science translational medicine 4, 125 (2012)."},{"key":"e_1_3_2_1_45_1","volume-title":"Proceedings of Workshop on Machine Learning Systems in the 29th Conference on Neural Information Processing Systems (NIPS)","volume":"5","author":"Tokui Seiya","year":"2015","unstructured":"Seiya Tokui, Kenta Oono, Shohei Hido, and Justin Clayton. 2015. Chainer: A Next-Generation Open Source Framework for Deep Learning. In Proceedings of Workshop on Machine Learning Systems in the 29th Conference on Neural Information Processing Systems (NIPS), Vol. 5. 1--6."},{"key":"e_1_3_2_1_46_1","volume-title":"Graph Attention Networks. In 6th International Conference on Learning Representations","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. [n.d.]. Graph Attention Networks. In 6th International Conference on Learning Representations, 2018."},{"key":"e_1_3_2_1_47_1","volume-title":"Deep-DDS: Deep Graph Neural Network with Attention Mechanism to Predict Synergistic Drug Combinations. bioRxiv","author":"Wang Jinxian","year":"2021","unstructured":"Jinxian Wang, Wenhao Zhang, Siyuan Shen, Lei Deng, and Hui Liu. 2021. Deep-DDS: Deep Graph Neural Network with Attention Mechanism to Predict Synergistic Drug Combinations. bioRxiv (2021)."},{"key":"e_1_3_2_1_48_1","unstructured":"Minjie Wang Lingfan Yu Da Zheng et al. 2019. Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs. (2019)."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci00057a005"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/551"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Bulat Zagidullin Jehad Aldahdooh Shuyu Zheng Wenyu Wang Yinyin Wang et al. 2019. DrugComb: An Integrative Cancer Drug Combination Data Portal. Nucleic acids research 47 W1 (2019) W43--W51.","DOI":"10.1093\/nar\/gkz337"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"crossref","unstructured":"Shuyu Zheng Jehad Aldahdooh Tolou Shadbahr Yinyin Wang Dalal Aldahdooh et al. 2021. DrugComb Update: A More Comprehensive Drug Sensitivity Data Repository and Analysis Portal. Nucleic Acids Research (2021).","DOI":"10.1101\/2021.03.25.436916"},{"key":"e_1_3_2_1_53_1","unstructured":"Zhaocheng Zhu Shengchao Liu Chence Shi et al. 2021. TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery."}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","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 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539023","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539023","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539023","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:49Z","timestamp":1750183789000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539023"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":53,"alternative-id":["10.1145\/3534678.3539023","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539023","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}