{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T19:44:33Z","timestamp":1793821473880,"version":"4.6.1"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS-2323050"],"award-info":[{"award-number":["CNS-2323050"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CNS-2148178"],"award-info":[{"award-number":["CNS-2148178"]}],"id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Resilient and Intelligent NextG Systems","award":["CNS-2148178"],"award-info":[{"award-number":["CNS-2148178"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE\/ACM Trans. Comput. Biol. and Bioinf."],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1109\/tcbb.2024.3349990","type":"journal-article","created":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T20:17:30Z","timestamp":1704485850000},"page":"240-253","source":"Crossref","is-referenced-by-count":28,"title":["GraphGANFed: A Federated Generative Framework for Graph-Structured Molecules Towards Efficient Drug Discovery"],"prefix":"10.1109","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5498-2677","authenticated-orcid":false,"given":"Daniel","family":"Manu","sequence":"first","affiliation":[{"name":"SECNet Lab., Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5828-9124","authenticated-orcid":false,"given":"Jingjing","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Texas Tech University, Lubbock, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3213-5172","authenticated-orcid":false,"given":"Wuji","family":"Liu","sequence":"additional","affiliation":[{"name":"Amazon, San Diego, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6954-7018","authenticated-orcid":false,"given":"Xiang","family":"Sun","sequence":"additional","affiliation":[{"name":"SECNet Lab., Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"CrystalGAN: Learning to discover crystallographic structures with generative adversarial networks","author":"Nouira","year":"2018"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.accounts.0c00699"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAD51958.2021.9643440"},{"key":"ref4","article-title":"Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models","author":"Guimaraes","year":"2017"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.1c00600"},{"key":"ref6","article-title":"MolGAN: An implicit generative model for small molecular graphs","author":"De Cao","year":"2018"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3390\/ijms22041676"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1021\/ci00057a005"},{"key":"ref9","article-title":"Utilising graph machine learning within drug discovery and development","author":"Gaudelet","year":"2020"},{"key":"ref10","article-title":"Molecular generation with recurrent neural networks (RNNs)","author":"Bjerrum","year":"2017"},{"key":"ref11","first-page":"169","article-title":"HMC-TRAN: A tensor-core inspired hierarchical model compression for transformer-based DNNs on GPU","volume-title":"Proc. Great Lakes Symp. VLSI","author":"Huang"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01418-6_41"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1039\/9781788016841-00228"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-018-0286-7"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1021\/acs.molpharmaceut.7b01137"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1021\/acs.molpharmaceut.8b00839"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3390\/jimaging8120312"},{"key":"ref18","article-title":"A two-step graph convolutional decoder for molecule generation","author":"Bresson","year":"2019"},{"key":"ref19","article-title":"Graph convolutional policy network for goal-directed molecular graph generation","author":"You","year":"2018"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3453688.3461741"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.299"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363678"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.105268"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa1006"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1561\/9781680835090"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2021.3076167"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref28","article-title":"Wasserstein GAN","author":"Arjovsky","year":"2017"},{"key":"ref29","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-540-71050-9","volume-title":"Optimal Transport: Old and New","volume":"338","author":"Villani","year":"2009"},{"key":"ref30","article-title":"Improved training of wasserstein GANs","author":"Gulrajani","year":"2017"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/s10822-016-9938-8"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms13890"},{"key":"ref33","article-title":"PADME: A deep learning-based framework for drug-target interaction prediction","author":"Feng","year":"2018"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/483"},{"key":"ref35","article-title":"Predicting organic reaction outcomes with weisfeiler-lehman network","author":"Jin","year":"2017"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkr1132"},{"key":"ref37","article-title":"Toxicity prediction using deep learning","author":"Unterthiner","year":"2015"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1021\/jm300687e"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1021\/ci8002649"},{"key":"ref40","article-title":"Categorical reparameterization with gumbel-softmax","author":"Jang","year":"2016"},{"key":"ref41","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1080\/2150704X.2022.2077152"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12702"},{"key":"ref44","article-title":"Federated learning of deep networks using model averaging","author":"McMahan","year":"2016"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2020.565644"},{"issue":"1\u201379","key":"ref46","article-title":"RDkit documentation","volume":"1","author":"Landrum","year":"2013","journal-title":"Release"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1039\/C7SC02664A"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1021\/ci034243x"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1063\/1.4928757"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1021\/ci300415d"}],"container-title":["IEEE\/ACM Transactions on Computational Biology and Bioinformatics"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/8857\/10490288\/10381739-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8857\/10490288\/10381739.pdf?arnumber=10381739","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T21:18:08Z","timestamp":1743801488000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10381739\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3]]},"references-count":50,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tcbb.2024.3349990","relation":{},"ISSN":["1545-5963","1557-9964","2374-0043"],"issn-type":[{"value":"1545-5963","type":"print"},{"value":"1557-9964","type":"electronic"},{"value":"2374-0043","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3]]}}}