{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T04:15:04Z","timestamp":1760328904624,"version":"3.37.3"},"reference-count":48,"publisher":"American Chemical Society (ACS)","issue":"5","license":[{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,2,16]],"date-time":"2024-02-16T00:00:00Z","timestamp":1708041600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"DOI":"10.13039\/501100014364","name":"Samsung Science and Technology Foundation","doi-asserted-by":"publisher","award":["SRFC-MA2101-05"],"award-info":[{"award-number":["SRFC-MA2101-05"]}],"id":[{"id":"10.13039\/501100014364","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2023M3K5A1094813","RS-2023-00257666"],"award-info":[{"award-number":["NRF-2023M3K5A1094813","RS-2023-00257666"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Chem. Inf. Model."],"published-print":{"date-parts":[[2024,3,11]]},"DOI":"10.1021\/acs.jcim.3c00870","type":"journal-article","created":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T03:56:31Z","timestamp":1708142191000},"page":"1522-1532","source":"Crossref","is-referenced-by-count":3,"title":["Gaussian Process Regression-Based Near-Infrared <scp>d<\/scp>-Luciferin Analogue Design Using Mutation-Controlled Graph-Based Genetic Algorithm"],"prefix":"10.1021","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9487-7537","authenticated-orcid":true,"given":"Sung Wook","family":"Moon","sequence":"first","affiliation":[{"name":"Departmet of Chemistry, School of Natural Science, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan 44919, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5636-3407","authenticated-orcid":true,"given":"Seung Kyu","family":"Min","sequence":"additional","affiliation":[{"name":"Departmet of Chemistry, School of Natural Science, Ulsan National Institute of Science and Technology (UNIST), 50 UNIST-gil, Ulju-gun, Ulsan 44919, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"316","published-online":{"date-parts":[[2024,2,16]]},"reference":[{"key":"ref1\/cit1","doi-asserted-by":"publisher","DOI":"10.1039\/C9ME00039A"},{"key":"ref2\/cit2","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2021.05.019"},{"key":"ref3\/cit3","unstructured":"Kingma, D. P.; Welling, M. Auto-encoding variational Bayes, 2014, https:\/\/arxiv.org\/abs\/1312.6114."},{"key":"ref4\/cit4","unstructured":"Goodfellow, I. J.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; Bengio, Y. Generative Adversarial Networks 2014 https:\/\/arxiv.org\/abs\/1406.2661."},{"key":"ref5\/cit5","unstructured":"Mnih, V.; Kavukcuoglu, K.; Silver, D.; Graves, A.; Antonoglou, I.; Wierstra, D.; Riedmiller, M. Playing Atari with deep reinforcement learning, 2013, https:\/\/arxiv.org\/abs\/1312.5602."},{"key":"ref6\/cit6","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00572"},{"key":"ref7\/cit7","unstructured":"De Cao, N.; Kipf, T. Molgan: An implicit generative model for small molecular graphs, 2018, https:\/\/arxiv.org\/abs\/1805.11973."},{"key":"ref8\/cit8","unstructured":"Guimaraes, G. L.; Sanchez-Lengeling, B.; Outeiral, C.; Farias, P. L. C.; Aspuru-Guzik, A. Objective-reinforced generative adversarial networks (organ) for Sequence generation models, 2018, https:\/\/arxiv.org\/abs\/1705.10843."},{"key":"ref9\/cit9","doi-asserted-by":"publisher","DOI":"10.1021\/ci00024a003"},{"key":"ref10\/cit10","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.8b00213"},{"key":"ref11\/cit11","doi-asserted-by":"publisher","DOI":"10.1002\/anie.201310864"},{"key":"ref12\/cit12","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-021-00501-7"},{"key":"ref13\/cit13","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2022.3144045"},{"key":"ref14\/cit14","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-10139-6"},{"key":"ref15\/cit15","doi-asserted-by":"publisher","DOI":"10.1021\/ci00057a005"},{"key":"ref16\/cit16","doi-asserted-by":"crossref","unstructured":"O\u2019Boyle, N.; Dalke, A. Deepsmiles: An adaptation of smiles for use in machine-learning of chemical structures 2018.","DOI":"10.26434\/chemrxiv.7097960"},{"key":"ref17\/cit17","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/aba947"},{"key":"ref18\/cit18","doi-asserted-by":"publisher","DOI":"10.1021\/ci034290p"},{"key":"ref19\/cit19","doi-asserted-by":"publisher","DOI":"10.1016\/j.chempr.2020.12.009"},{"key":"ref20\/cit20","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-96812-8"},{"key":"ref21\/cit21","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemrev.1c00022"},{"key":"ref22\/cit22","doi-asserted-by":"publisher","DOI":"10.1016\/j.trechm.2019.03.002"},{"key":"ref23\/cit23","doi-asserted-by":"publisher","DOI":"10.1021\/jacs.0c01064"},{"key":"ref24\/cit24","doi-asserted-by":"publisher","DOI":"10.1002\/chir.23236"},{"key":"ref25\/cit25","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-1-8"},{"key":"ref26\/cit26","doi-asserted-by":"publisher","DOI":"10.1039\/C8SC05372C"},{"key":"ref27\/cit27","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-pchem.11"},{"key":"ref28\/cit28","doi-asserted-by":"publisher","DOI":"10.1021\/ci100050t"},{"key":"ref29\/cit29","unstructured":"Landrum, g. Open-source cheminformatics software, 2020, https:\/\/www.rdkit.org\/."},{"key":"ref30\/cit30","doi-asserted-by":"publisher","DOI":"10.1021\/ci010132r"},{"key":"ref31\/cit31","unstructured":"Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; Li, X.; Caricato, M.; Marenich, A. V.; Bloino, J.; Janesko, B. G.; Gomperts, R.; Mennucci, B.; Hratchian, H. P.; Ortiz, J. V.; Izmaylov, A. F.; Sonnenberg, J. L.; Williams-Young, D.; Ding, F.; Lipparini, F.; Egidi, F.; Goings, J.; Peng, B.; Petrone, A.; Henderson, T.; Ranasinghe, D.; Zakrzewski, V. G.; Gao, J.; Rega, N.; Zheng, G.; Liang, W.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Throssell, K.; Montgomery, J. A., Jr.; Peralta, J. E.; Ogliaro, F.; Bearpark, M. J.; Heyd, J. J.; Brothers, E. N.; Kudin, K. N.; Staroverov, V. N.; Keith, T. A.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A. P.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Millam, J. M.; Klene, M.; Adamo, C.; Cammi, R.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Farkas, O.; Foresman, J. B.; Fox, D. J. Gaussian16, Revision C.01; Gaussian Inc.: Wallingford CT, 2016."},{"key":"ref32\/cit32","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-3-33"},{"key":"ref33\/cit33","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00839"},{"key":"ref34\/cit34","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gky1075"},{"key":"ref35\/cit35","unstructured":"Post-processed ChEMBL datasets. https:\/\/figshare.com\/projects\/GuacaMol\/56639."},{"key":"ref36\/cit36","doi-asserted-by":"publisher","DOI":"10.1021\/ci300415d"},{"key":"ref37\/cit37","doi-asserted-by":"publisher","DOI":"10.1063\/1.4928757"},{"key":"ref38\/cit38","unstructured":"Moss, H. B.; Griffiths, R.R. Gaussian process molecule property prediction with FlowMO 2020 https:\/\/arxiv.org\/abs\/2010.01118."},{"key":"ref39\/cit39","doi-asserted-by":"publisher","DOI":"10.1021\/acs.analchem.9b04562"},{"key":"ref40\/cit40","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms11856"},{"key":"ref41\/cit41","doi-asserted-by":"publisher","DOI":"10.1021\/ja505795s"},{"key":"ref42\/cit42","doi-asserted-by":"publisher","DOI":"10.1002\/anie.201405955"},{"key":"ref43\/cit43","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-017-02542-9"},{"key":"ref44\/cit44","doi-asserted-by":"publisher","DOI":"10.1063\/1.3382344"},{"key":"ref45\/cit45","doi-asserted-by":"publisher","DOI":"10.1063\/1.3359469"},{"key":"ref46\/cit46","doi-asserted-by":"publisher","DOI":"10.1021\/jp074063g"},{"key":"ref47\/cit47","doi-asserted-by":"publisher","DOI":"10.1021\/ja808511t"},{"key":"ref48\/cit48","doi-asserted-by":"crossref","first-page":"3955","DOI":"10.1039\/C6CP08154A","volume":"19","author":"Augusto F. A.","year":"2017","journal-title":"Phys. Chem. Chem. Phys."}],"container-title":["Journal of Chemical Information and Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.3c00870","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.3c00870","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T08:14:27Z","timestamp":1710144867000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/doi\/10.1021\/acs.jcim.3c00870"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,16]]},"references-count":48,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,3,11]]}},"alternative-id":["10.1021\/acs.jcim.3c00870"],"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.3c00870","relation":{},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"type":"print","value":"1549-9596"},{"type":"electronic","value":"1549-960X"}],"subject":[],"published":{"date-parts":[[2024,2,16]]}}}