{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T01:42:18Z","timestamp":1765503738191,"version":"3.48.0"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","funder":[{"name":"https:\/\/ccas.nd.edu\/","award":["CHE-2202693"],"award-info":[{"award-number":["CHE-2202693"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,10]]},"DOI":"10.1145\/3746252.3761323","type":"proceedings-article","created":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T00:18:04Z","timestamp":1762561084000},"page":"791-801","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Proto-Yield: An Uncertainty-Aware Prototype Network for Yield Prediction in Real-world Chemical Reactions"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6396-1869","authenticated-orcid":false,"given":"Kehan","family":"Guo","sequence":"first","affiliation":[{"name":"University of Notre Dame, South Bend, IN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6025-5468","authenticated-orcid":false,"given":"Zhen","family":"Liu","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, Pittsburgh, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7673-8568","authenticated-orcid":false,"given":"Zhichun","family":"Guo","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9645-0724","authenticated-orcid":false,"given":"Bozhao","family":"Nan","sequence":"additional","affiliation":[{"name":"University of Notre Dame, South Bend, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7581-8497","authenticated-orcid":false,"given":"Olexandr","family":"Isayev","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University, pittsburgh, PA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3932-5956","authenticated-orcid":false,"given":"Nitesh","family":"Chawla","sequence":"additional","affiliation":[{"name":"University of Notre Dame, South Bend, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9316-7720","authenticated-orcid":false,"given":"Olaf","family":"Wiest","sequence":"additional","affiliation":[{"name":"University of Notre Dame, South Bend, IN, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3574-5665","authenticated-orcid":false,"given":"Xiangliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Notre Dame, South Bend, IN, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Predicting reaction performance in C-N cross-coupling using machine learning. Science 360, 6385","author":"Ahneman Derek T","year":"2018","unstructured":"Derek T Ahneman, Jes\u00fas G Estrada, Shishi Lin, Spencer D Dreher, and Abigail G Doyle. 2018. Predicting reaction performance in C-N cross-coupling using machine learning. Science 360, 6385 (2018), 186-190."},{"key":"e_1_3_2_1_2_1","first-page":"1","article-title":"Protoattend: Attention-based prototypical learning","volume":"21","author":"Arik Sercan O","year":"2020","unstructured":"Sercan O Arik and Tomas Pfister. 2020. Protoattend: Attention-based prototypical learning. Journal of Machine Learning Research 21, 210 (2020), 1-35.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00418-8"},{"key":"e_1_3_2_1_4_1","volume-title":"Random forests. Machine learning 45","author":"Breiman Leo","year":"2001","unstructured":"Leo Breiman. 2001. Random forests. Machine learning 45 (2001), 5-32."},{"key":"e_1_3_2_1_5_1","volume-title":"Approximating cnns with bag-of-local-features models works surprisingly well on imagenet. arXiv preprint arXiv:1904.00760","author":"Brendel Wieland","year":"2019","unstructured":"Wieland Brendel and Matthias Bethge. 2019. Approximating cnns with bag-of-local-features models works surprisingly well on imagenet. arXiv preprint arXiv:1904.00760 (2019)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymeth.2014.08.005"},{"key":"e_1_3_2_1_7_1","volume-title":"This looks like that: deep learning for interpretable image recognition. Advances in neural information processing systems 32","author":"Chen Chaofan","year":"2019","unstructured":"Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan K Su. 2019. This looks like that: deep learning for interpretable image recognition. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28668"},{"key":"e_1_3_2_1_9_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00353"},{"key":"e_1_3_2_1_11_1","volume-title":"Convolutional networks on graphs for learning molecular fingerprints. Advances in neural information processing systems 28","author":"Duvenaud David K","year":"2015","unstructured":"David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Al\u00e1n Aspuru-Guzik, and Ryan P Adams. 2015. Convolutional networks on graphs for learning molecular fingerprints. Advances in neural information processing systems 28 (2015)."},{"key":"e_1_3_2_1_12_1","first-page":"343","article-title":"Patent transactions in the marketplace: Lessons from the USPTO patent assignment dataset","volume":"27","author":"Graham Stuart JH","year":"2018","unstructured":"Stuart JH Graham, Alan C Marco, and Amanda F Myers. 2018. Patent transactions in the marketplace: Lessons from the USPTO patent assignment dataset. Journal of Economics & Management Strategy 27, 3 (2018), 343-371.","journal-title":"Journal of Economics & Management Strategy"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.201"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3083838"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1246\/cl.1999.1015"},{"key":"e_1_3_2_1_16_1","volume-title":"Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems 30","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-021-00579-z"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-021-00579-z"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","unstructured":"Greg Landrum Paolo Tosco Brian Kelley sriniker gedeck NadineSchneider Riccardo Vianello Andrew Dalke Ric Brian Cole AlexanderSavelyev Samo Turk Matt Swain Alain Vaucher Dan N Maciej W\u00f3jcikowski Axel Pahl JP Francois Berenger strets123 JLVarjo Noel O'Boyle David Cosgrove Patrick Fuller Jan Holst Jensen Gianluca Sforna DoliathGavid Karl Leswing Susan Leung and Jeff van Santen. 2019. rdkit\/rdkit: 2019_03_4 (Q1 2019) Release. https:\/\/doi.org\/10.5281\/zenodo.3366468","DOI":"10.5281\/zenodo.3366468"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11771"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-39283-x"},{"key":"e_1_3_2_1_22_1","volume-title":"Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493","author":"Li Yujia","year":"2015","unstructured":"Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2015. Gated graph sequence neural networks. arXiv preprint arXiv:1511.05493 (2015)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01087"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1039\/D3SC03902A"},{"key":"e_1_3_2_1_25_1","volume-title":"Learning with mixture of prototypes for out-of-distribution detection. arXiv preprint arXiv:2402.02653","author":"Lu Haodong","year":"2024","unstructured":"Haodong Lu, Dong Gong, Shuo Wang, Jason Xue, Lina Yao, and Kristen Moore. 2024. Learning with mixture of prototypes for out-of-distribution detection. arXiv preprint arXiv:2402.02653 (2024)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589335.3651470"},{"key":"e_1_3_2_1_27_1","volume-title":"Paul D'Agostino, and Kirsten Apple.","author":"Marco Alan C","year":"2015","unstructured":"Alan C Marco, Amanda Myers, Stuart JH Graham, Paul D'Agostino, and Kirsten Apple. 2015. The USPTO patent assignment dataset: Descriptions and analysis. (2015)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemrev.1c00033"},{"key":"e_1_3_2_1_29_1","volume-title":"Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions. Molecular pharmaceutics 9, 10","author":"Myint Kyaw-Zeyar","year":"2012","unstructured":"Kyaw-Zeyar Myint, LirongWang, Qin Tong, and Xiang-Qun Xie. 2012. Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions. Molecular pharmaceutics 9, 10 (2012), 2912-2923."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01469"},{"key":"e_1_3_2_1_31_1","volume-title":"ProtoInfoMax: prototypical networks with mutual information maximization for out-of-domain detection. arXiv preprint arXiv:2108.12229","author":"Ni'mah Iftitahu","year":"2021","unstructured":"Iftitahu Ni'mah, Meng Fang, Vlado Menkovski, and Mykola Pechenizkiy. 2021. ProtoInfoMax: prototypical networks with mutual information maximization for out-of-domain detection. arXiv preprint arXiv:2108.12229 (2021)."},{"key":"e_1_3_2_1_32_1","volume-title":"A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow. Science 359, 6374","author":"Perera Damith","year":"2018","unstructured":"Damith Perera, JosephWTucker, Shalini Brahmbhatt, Christopher J Helal, Ashley Chong, William Farrell, Paul Richardson, and Neal W Sach. 2018. A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow. Science 359, 6374 (2018), 429-434."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.3c01163"},{"key":"e_1_3_2_1_34_1","volume-title":"https:\/\/new.reaxys.com\/Accessed","author":"Database Reaxys","year":"2020","unstructured":"Reaxys. 2020. Reaxys Database. https:\/\/new.reaxys.com\/Accessed: Feb 10, 2020."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Mandana Saebi Bozhao Nan John E Herr Jessica Wahlers Zhichun Guo Andrzej M Zura'ski Thierry Kogej Per-Ola Norrby Abigail G Doyle Nitesh V Chawla et al. 2023. On the use of real-world datasets for reaction yield prediction. Chemical science 14 19 (2023) 4997-5005.","DOI":"10.1039\/D2SC06041H"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chempr.2020.02.017"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci5006614"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.26434\/chemrxiv.13286741.v1"},{"key":"e_1_3_2_1_39_1","volume-title":"Prediction of chemical reaction yields using deep learning. Machine learning: science and technology 2, 1","author":"Schwaller Philippe","year":"2021","unstructured":"Philippe Schwaller, Alain C Vaucher, Teodoro Laino, and Jean-Louis Reymond. 2021. Prediction of chemical reaction yields using deep learning. Machine learning: science and technology 2, 1 (2021), 015016."},{"key":"e_1_3_2_1_40_1","volume-title":"The development of multidimensional analysis tools for asymmetric catalysis and beyond. Accounts of chemical research 49, 6","author":"Sigman Matthew S","year":"2016","unstructured":"Matthew S Sigman, Kaid C Harper, Elizabeth N Bess, and Anat Milo. 2016. The development of multidimensional analysis tools for asymmetric catalysis and beyond. Accounts of chemical research 49, 6 (2016), 1292-1301."},{"key":"e_1_3_2_1_41_1","volume-title":"Prototypical networks for few-shot learning. Advances in neural information processing systems 30","author":"Snell Jake","year":"2017","unstructured":"Jake Snell, Kevin Swersky, and Richard Zemel. 2017. Prototypical networks for few-shot learning. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_42_1","volume-title":"Attention is all you need. Advances in neural information processing systems 30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00447-x"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.445681"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1002\/wcms.1603"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1246\/cl.171130"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00366"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.27016"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109651"}],"event":{"name":"CIKM '25: The 34th ACM International Conference on Information and Knowledge Management","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Seoul Republic of Korea","acronym":"CIKM '25"},"container-title":["Proceedings of the 34th ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746252.3761323","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T01:41:08Z","timestamp":1765503668000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746252.3761323"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":49,"alternative-id":["10.1145\/3746252.3761323","10.1145\/3746252"],"URL":"https:\/\/doi.org\/10.1145\/3746252.3761323","relation":{},"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"2025-11-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}