{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T17:38:56Z","timestamp":1772127536202,"version":"3.50.1"},"reference-count":75,"publisher":"American Chemical Society (ACS)","issue":"10","license":[{"start":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T00:00:00Z","timestamp":1596758400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T00:00:00Z","timestamp":1596758400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T00:00:00Z","timestamp":1596758400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Chem. Inf. Model."],"published-print":{"date-parts":[[2020,10,26]]},"DOI":"10.1021\/acs.jcim.0c00622","type":"journal-article","created":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T13:22:49Z","timestamp":1596806569000},"page":"4629-4639","source":"Crossref","is-referenced-by-count":19,"title":["Improved Scaffold Hopping in Ligand-Based Virtual Screening Using Neural Representation Learning"],"prefix":"10.1021","volume":"60","author":[{"given":"Luka","family":"Stojanovi\u0107","sequence":"first","affiliation":[{"name":"Totient, Inc., Sin\u0111eli\u0107eva 9, 11000 Belgrade, Serbia"}]},{"given":"Milo\u0161","family":"Popovi\u0107","sequence":"additional","affiliation":[{"name":"Totient, Inc., Sin\u0111eli\u0107eva 9, 11000 Belgrade, Serbia"}]},{"given":"Neboj\u0161a","family":"Tijani\u0107","sequence":"additional","affiliation":[{"name":"Totient, Inc., Sin\u0111eli\u0107eva 9, 11000 Belgrade, Serbia"}]},{"given":"Goran","family":"Rako\u010devi\u0107","sequence":"additional","affiliation":[{"name":"Totient, Inc., Sin\u0111eli\u0107eva 9, 11000 Belgrade, Serbia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4652-3697","authenticated-orcid":true,"given":"Marko","family":"Kalini\u0107","sequence":"additional","affiliation":[{"name":"Totient, Inc., Sin\u0111eli\u0107eva 9, 11000 Belgrade, Serbia"}]}],"member":"316","published-online":{"date-parts":[[2020,8,7]]},"reference":[{"key":"ref1\/cit1","doi-asserted-by":"publisher","DOI":"10.1021\/jm101020z"},{"key":"ref2\/cit2","doi-asserted-by":"publisher","DOI":"10.1021\/ci200528d"},{"key":"ref3\/cit3","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b01101"},{"key":"ref4\/cit4","doi-asserted-by":"publisher","DOI":"10.1038\/nrd.2016.213"},{"key":"ref5\/cit5","doi-asserted-by":"publisher","DOI":"10.1080\/17460441.2016.1201262"},{"key":"ref6\/cit6","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.24764"},{"key":"ref7\/cit7","doi-asserted-by":"publisher","DOI":"10.4155\/fmc-2018-0314"},{"key":"ref8\/cit8","doi-asserted-by":"publisher","DOI":"10.2174\/1573409914666181018141602"},{"key":"ref9\/cit9","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00266"},{"key":"ref10\/cit10","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref11\/cit11","unstructured":"Goh, G. B.; Siegel, C.; Vishnu, A.; Hodas, N. O.; Baker, N. Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expertdeveloped QSAR\/QSPR Models. 2017, arXiv:1706.06689. https:\/\/arxiv.org\/abs\/1706.06689."},{"key":"ref12\/cit12","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2007.01.011"},{"key":"ref13\/cit13","doi-asserted-by":"publisher","DOI":"10.1021\/jm401411z"},{"key":"ref14\/cit14","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jmedchem.6b01437"},{"key":"ref15\/cit15","doi-asserted-by":"publisher","DOI":"10.1021\/ci00046a002"},{"key":"ref16\/cit16","doi-asserted-by":"publisher","DOI":"10.1021\/ci00054a008"},{"key":"ref17\/cit17","doi-asserted-by":"publisher","DOI":"10.1021\/ci050413p"},{"key":"ref18\/cit18","doi-asserted-by":"publisher","DOI":"10.1021\/ci100050t"},{"key":"ref19\/cit19","doi-asserted-by":"publisher","DOI":"10.1016\/0898-5529(90)90120-W"},{"key":"ref20\/cit20","doi-asserted-by":"publisher","DOI":"10.1021\/jm9806998"},{"key":"ref21\/cit21","doi-asserted-by":"publisher","DOI":"10.2174\/0929867013372481"},{"key":"ref22\/cit22","doi-asserted-by":"publisher","DOI":"10.1021\/ci025595r"},{"key":"ref23\/cit23","doi-asserted-by":"publisher","DOI":"10.1021\/ci049885e"},{"key":"ref24\/cit24","doi-asserted-by":"publisher","DOI":"10.1021\/ci049651v"},{"key":"ref25\/cit25","doi-asserted-by":"publisher","DOI":"10.1007\/s10822-006-9087-6"},{"key":"ref26\/cit26","doi-asserted-by":"publisher","DOI":"10.1021\/ci800110p"},{"key":"ref27\/cit27","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmgm.2009.01.001"},{"key":"ref28\/cit28","doi-asserted-by":"publisher","DOI":"10.1021\/ci300153d"},{"key":"ref29\/cit29","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jmedchem.7b00696"},{"key":"ref30\/cit30","doi-asserted-by":"publisher","DOI":"10.1021\/ci100263p"},{"key":"ref31\/cit31","doi-asserted-by":"publisher","DOI":"10.1021\/jm1013677"},{"key":"ref32\/cit32","doi-asserted-by":"publisher","DOI":"10.1021\/ci300030u"},{"key":"ref33\/cit33","doi-asserted-by":"publisher","DOI":"10.1021\/jm100492z"},{"key":"ref34\/cit34","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2011.02.011"},{"key":"ref35\/cit35","doi-asserted-by":"publisher","DOI":"10.4155\/fmc.11.4"},{"key":"ref36\/cit36","unstructured":"Duvenaud, D. K.; Maclaurin, D.; Iparraguirre, J.; Bombarell, R.; Hirzel, T.; AspuruGuzik, A.; Adams, R. P. In\n                      Advances in Neural Information Processing Systems 28\n                      ; Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M., Garnett, R., Eds. Curran Associates, Inc., 2015; pp. 2224\u20132232."},{"key":"ref37\/cit37","doi-asserted-by":"publisher","DOI":"10.1007\/s10822-016-9938-8"},{"key":"ref38\/cit38","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.7b00244"},{"key":"ref39\/cit39","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.6b00601"},{"key":"ref40\/cit40","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.6b00367"},{"key":"ref41\/cit41","doi-asserted-by":"publisher","DOI":"10.1039\/C8SC04228D"},{"key":"ref42\/cit42","first-page":"1263","volume-title":"Proceedings of the 34th International Conference on Machine Learning","volume":"70","author":"Gilmer J.","year":"2017"},{"key":"ref43\/cit43","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00237"},{"key":"ref44\/cit44","unstructured":"Ryu, S.; Lim, J.; Hong, S. H.; Kim, W.Y. Deeply learningmolecular structure-property relationships using attention- and gate-augmented graph convolutional network. 2018, arXiv: 1805.10988. https:\/\/arxiv.org\/abs\/1805.10988."},{"key":"ref45\/cit45","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-019-0407-y"},{"key":"ref46\/cit46","doi-asserted-by":"publisher","DOI":"10.1002\/minf.201800031"},{"key":"ref47\/cit47","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-018-0310-y"},{"key":"ref48\/cit48","doi-asserted-by":"publisher","DOI":"10.1021\/ci500190p"},{"key":"ref49\/cit49","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.5b00498"},{"key":"ref50\/cit50","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.7b00616"},{"key":"ref51\/cit51","doi-asserted-by":"publisher","DOI":"10.1039\/C8SC04175J"},{"key":"ref52\/cit52","unstructured":"Veli\u010dkovi\u0107, P.; Cucurull, G.; Casanova, A.; Romero, A.; Li\u00f2, P.; Bengio, Y. Graph Attention Networks. 2017, arXiv:1710.10903. https:\/\/arxiv.org\/abs\/1710.10903."},{"key":"ref53\/cit53","unstructured":"Bresson, X.; Laurent, T. Residual Gated Graph ConvNets. 2017, arXiv:1711.07553. https:\/\/arxiv.org\/abs\/1711.07553."},{"key":"ref54\/cit54","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-5-26"},{"key":"ref55\/cit55","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-018-0321-8"},{"key":"ref56\/cit56","doi-asserted-by":"publisher","DOI":"10.1021\/jm9602928"},{"key":"ref57\/cit57","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00571"},{"key":"ref58\/cit58","doi-asserted-by":"publisher","DOI":"10.1021\/ci3001277"},{"key":"ref59\/cit59","doi-asserted-by":"crossref","unstructured":"Poli\u010dar, P. G.; Stra\u017ear, M.; Zupan, B. openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding. 2019, bioRxiv: 731877. https:\/\/biorxiv.org\/content\/10.1101\/731877v3.full.","DOI":"10.1101\/731877"},{"key":"ref60\/cit60","doi-asserted-by":"crossref","unstructured":"H\u00fcllermeier, E.; Fober, T.; Mernberger, M. In\n                      Encyclopedia of Systems Biology\n                      ; Dubitzky, W., Wolkenhauer, O., Cho, K.H., Yokota, H., Eds. Springer New York: New York, NY, 2013; pp. 1018\u20131018.","DOI":"10.1007\/978-1-4419-9863-7_927"},{"key":"ref61\/cit61","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0686-2"},{"key":"ref62\/cit62","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gky1033"},{"key":"ref63\/cit63","unstructured":"Landrum, G. A.\n                      RDKit: Open-Source Cheminformatics\n                      , rdkit.org, accessed on February 26, 2020."},{"key":"ref64\/cit64","doi-asserted-by":"publisher","DOI":"10.1021\/ci8002649"},{"key":"ref65\/cit65","doi-asserted-by":"publisher","DOI":"10.1021\/ci200199u"},{"key":"ref66\/cit66","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkw1074"},{"key":"ref67\/cit67","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-1-14"},{"key":"ref68\/cit68","unstructured":"Paszke, A. In\n                      Advances in Neural Information Processing Systems 32\n                      ; Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., Garnett, R., Eds. Curran Associates, Inc., 2019; pp. 8024\u20138035."},{"key":"ref69\/cit69","unstructured":"Wang, M.  Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs. 2019, arXiv: 1909.01315. https:\/\/arxiv.org\/abs\/1909.01315."},{"key":"ref70\/cit70","unstructured":"Ba, J. L.; Kiros, J. R.; Hinton, G. E. Layer Normalization. 2016, arXiv:1607.06450. https:\/\/arxiv.org\/abs\/1607.06450."},{"key":"ref71\/cit71","doi-asserted-by":"crossref","unstructured":"He, K.; Zhang, X.; Ren, S.; Sun, J. Delving Deep into Rectifiers: Surpassing HumanLevel Performance on ImageNet Classification. In\n                      Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV) . USA\n                      ; IEEE: 2015; pp. 1026\u20131034.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref72\/cit72","unstructured":"Ioffe, S.; Szegedy, C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. 2015, arXiv:1502.03167. https:\/\/arxiv.org\/abs\/1502.03167."},{"key":"ref73\/cit73","unstructured":"Loshchilov, I.; Hutter, F. Decoupled Weight Decay Regularization. 2017, arXiv:1711.05101. https:\/\/arxiv.org\/abs\/1711.05101."},{"key":"ref74\/cit74","unstructured":"Izmailov, P.; Podoprikhin, D.; Garipov, T.; Vetrov, D.; Wilson, A. G. Averaging Weights Leads to Wider Optima and Better Generalization. 2018, arXiv1803.05407. https:\/\/arxiv.org\/abs\/1803.05407."},{"key":"ref75\/cit75","doi-asserted-by":"publisher","DOI":"10.5936\/csbj.201302002"}],"container-title":["Journal of Chemical Information and Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.0c00622","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T12:57:37Z","timestamp":1682600257000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/doi\/10.1021\/acs.jcim.0c00622"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,7]]},"references-count":75,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2020,10,26]]}},"alternative-id":["10.1021\/acs.jcim.0c00622"],"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.0c00622","relation":{"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv.12412694","asserted-by":"object"},{"id-type":"doi","id":"10.26434\/chemrxiv.12412694.v1","asserted-by":"object"},{"id-type":"doi","id":"10.26434\/chemrxiv.12412694.v2","asserted-by":"object"}]},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,7]]}}}