{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T08:00:01Z","timestamp":1764403201720},"reference-count":32,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T00:00:00Z","timestamp":1670284800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T00:00:00Z","timestamp":1670284800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,6]]},"DOI":"10.1109\/bibm55620.2022.9995561","type":"proceedings-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T19:09:24Z","timestamp":1672686564000},"page":"315-321","source":"Crossref","is-referenced-by-count":3,"title":["HelixMO: Sample-Efficient Molecular Optimization in Scene-Sensitive Latent Space"],"prefix":"10.1109","author":[{"given":"Zhiyuan","family":"Chen","sequence":"first","affiliation":[{"name":"Baidu Inc."}]},{"given":"Xiaomin","family":"Fang","sequence":"additional","affiliation":[{"name":"Baidu Inc."}]},{"given":"Zixu","family":"Hua","sequence":"additional","affiliation":[{"name":"Baidu Inc."}]},{"given":"Yueyang","family":"Huang","sequence":"additional","affiliation":[{"name":"Baidu Inc."}]},{"given":"Fan","family":"Wang","sequence":"additional","affiliation":[{"name":"Baidu Inc."}]},{"given":"Hua","family":"Wu","sequence":"additional","affiliation":[{"name":"Baidu Inc."}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/nrd3368"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1038\/nrd941"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c00675"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkr777"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/432823a"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00512"},{"volume-title":"Molecular generation with recurrent neural networks (rnns)","year":"2017","author":"Bjerrum","key":"ref7"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00572"},{"article-title":"Syntax-directed variational autoencoder for structured data","volume-title":"International Conference on Learning Representations","author":"Dai","key":"ref9"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1039\/9781788016841-00228"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01418-6_41"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011110"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10804"},{"volume-title":"Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models","year":"2017","author":"Guimaraes","key":"ref14"},{"volume-title":"Molgan: An implicit generative model for small molecular graphs","year":"2018","author":"Cao","key":"ref15"},{"volume-title":"Graphnvp: An invertible flow model for generating molecular graphs","year":"2019","author":"Madhawa","key":"ref16"},{"volume-title":"Graph residual flow for molecular graph generation","year":"2019","author":"Honda","key":"ref17"},{"volume-title":"Graphaf: a flow-based autoregressive model for molecular graph generation","year":"2020","author":"Shi","key":"ref18"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403104"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1006\/jmbi.1999.3371"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkw1074"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2020.565644"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-4380-9_35"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1021\/ci100050t"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00438-4"},{"article-title":"Differentiable scaffolding tree for molecule optimization","volume-title":"International Conference on Learning Representations","author":"Fu","key":"ref26"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-018-0287-6"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1038\/nchem.1243"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-1-8"},{"article-title":"Mars: Markov molecular sampling for multi-objective drug discovery","volume-title":"International Conference on Learning Representations","author":"Xie","key":"ref30"},{"key":"ref31","first-page":"4849","article-title":"Multi-objective molecule generation using interpretable substructures","volume-title":"International conference on machine learning","author":"Jin"},{"article-title":"Molecule optimization by explainable evolution","volume-title":"International Conference on Learning Representations","author":"Chen","key":"ref32"}],"event":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","start":{"date-parts":[[2022,12,6]]},"location":"Las Vegas, NV, USA","end":{"date-parts":[[2022,12,8]]}},"container-title":["2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9994793\/9994847\/09995561.pdf?arnumber=9995561","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T11:18:31Z","timestamp":1709378311000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9995561\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,6]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/bibm55620.2022.9995561","relation":{},"subject":[],"published":{"date-parts":[[2022,12,6]]}}}