{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T02:02:15Z","timestamp":1777168935331,"version":"3.51.4"},"reference-count":63,"publisher":"American Chemical Society (ACS)","issue":"14","license":[{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["260613"],"award-info":[{"award-number":["260613"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"XtalPi Inc"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Chem. Inf. Model."],"published-print":{"date-parts":[[2022,7,25]]},"DOI":"10.1021\/acs.jcim.2c00177","type":"journal-article","created":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T21:19:38Z","timestamp":1657142378000},"page":"3291-3306","source":"Crossref","is-referenced-by-count":43,"title":["<i>De Novo<\/i> Molecule Design Using Molecular Generative Models Constrained by Ligand\u2013Protein Interactions"],"prefix":"10.1021","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5575-303X","authenticated-orcid":true,"given":"Jie","family":"Zhang","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Laboratory Animals, Guangdong Laboratory Animals Monitoring Institute, Guangzhou 510663, P. R. China"},{"name":"State Key Laboratory of Respiratory Disease, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, P. R. China"},{"name":"Bioland Laboratory (Guangzhou Regenerative Medicine and Health\u2500Guangdong Laboratory), Guangzhou 510530, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8065-8333","authenticated-orcid":true,"given":"Hongming","family":"Chen","sequence":"additional","affiliation":[{"name":"Bioland Laboratory (Guangzhou Regenerative Medicine and Health\u2500Guangdong Laboratory), Guangzhou 510530, P. R. China"},{"name":"Guangzhou International Bio Island, Guangzhou Laboratory, No. 9 XinDaoHuanBei Road, Guangzhou 510005, China"}]}],"member":"316","published-online":{"date-parts":[[2022,7,6]]},"reference":[{"key":"ref1\/cit1","doi-asserted-by":"crossref","unstructured":"Ciregan, D.; Meier, U.; Schmidhuber, J. Multi-column deep neural networks for image classification.  2012 IEEE Conference on Computer Vision and Pattern Recognition; IEEE, 2012; pp 3642\u20133649.","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"ref2\/cit2","first-page":"1097","volume-title":"Advances in Neural Information Processing Systems","volume":"25","author":"Krizhevsky A.","year":"2012"},{"key":"ref3\/cit3","doi-asserted-by":"crossref","unstructured":"Taigman, Y.; Yang, M.; Ranzato, M. A.; Wolf, L. Deepface: Closing the gap to human-level performance in face verification.  Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition; IEEE, 2014; pp 1701\u20131708.","DOI":"10.1109\/CVPR.2014.220"},{"key":"ref4\/cit4","unstructured":"Geirhos, R.; Janssen, D. H.; Sch\u00fctt, H. H.; Rauber, J.; Bethge, M.; Wichmann, F. A. Comparing deep neural networks against humans: object recognition when the signal gets weaker. 2017, arXiv preprint arXiv:1706.06969."},{"key":"ref5\/cit5","doi-asserted-by":"publisher","DOI":"10.1038\/nature16961"},{"key":"ref6\/cit6","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03819-2"},{"key":"ref7\/cit7","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-020-00428-5"},{"key":"ref8\/cit8","doi-asserted-by":"publisher","DOI":"10.1038\/nrd1799"},{"key":"ref9\/cit9","doi-asserted-by":"publisher","DOI":"10.1021\/ci800413m"},{"key":"ref10\/cit10","doi-asserted-by":"publisher","DOI":"10.1023\/a:1008184403558"},{"key":"ref11\/cit11","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-020-00429-4"},{"key":"ref12\/cit12","unstructured":"Hadjeres, G.; Pachet, F.; Nielsen, F. Deepbach: a steerable model for bach chorales generation.  International Conference on Machine Learning; PMLR, 2017; pp 1362\u20131371."},{"key":"ref13\/cit13","doi-asserted-by":"crossref","unstructured":"Garg, S.; Rish, I.; Cecchi, G.; Lozano, A. Neurogenesis-inspired dictionary learning: Online model adaption in a changing world. 2017, arXiv preprint arXiv:1701.06106.","DOI":"10.24963\/ijcai.2017\/235"},{"key":"ref14\/cit14","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00065"},{"key":"ref15\/cit15","unstructured":"Bjerrum, E. J.; Threlfall, R. Molecular generation with recurrent neural networks (RNNs). 2017, arXiv preprint arXiv:1705.04612."},{"key":"ref16\/cit16","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-0174-5"},{"key":"ref17\/cit17","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2020.565644"},{"key":"ref18\/cit18","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-019-0397-9"},{"key":"ref19\/cit19","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c01328"},{"key":"ref20\/cit20","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-019-0341-z"},{"key":"ref21\/cit21","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-019-0393-0"},{"key":"ref22\/cit22","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c00915"},{"key":"ref23\/cit23","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00512"},{"key":"ref24\/cit24","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00572"},{"key":"ref25\/cit25","doi-asserted-by":"publisher","DOI":"10.18632\/oncotarget.14073"},{"key":"ref26\/cit26","unstructured":"Makhzani, A.; Shlens, J.; Jaitly, N.; Goodfellow, I.; Frey, B. Adversarial autoencoders. 2015, arXiv preprint arXiv:1511.05644."},{"key":"ref27\/cit27","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. 2017, arXiv preprint arXiv:1705.10843."},{"key":"ref28\/cit28","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/abcf91"},{"key":"ref29\/cit29","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.aap7885"},{"key":"ref30\/cit30","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-017-0235-x"},{"key":"ref31\/cit31","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-47148-x"},{"key":"ref32\/cit32","doi-asserted-by":"publisher","DOI":"10.1038\/nchem.1243"},{"key":"ref33\/cit33","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty583"},{"key":"ref34\/cit34","volume-title":"Advances in Neural Information Processing Systems","volume":"32","author":"Gebauer N.","year":"2019"},{"key":"ref35\/cit35","unstructured":"Masuda, T.; Ragoza, M.; Koes, D. R. Generating 3d molecular structures conditional on a receptor binding site with deep generative models. 2020, arXiv preprint arXiv:2010.14442."},{"key":"ref36\/cit36","unstructured":"Simm, G.; Pinsler, R.; Hern\u00e1ndez-Lobato, J. M. Reinforcement learning for molecular design guided by quantum mechanics.  International Conference on Machine Learning; PMLR, 2020; pp 8959\u20138969."},{"key":"ref37\/cit37","unstructured":"Simm, G. N.; Pinsler, R.; Cs\u00e1nyi, G.; Hern\u00e1ndez-Lobato, J. M. Symmetry-aware actor-critic for 3d molecular design. 2020, arXiv preprint arXiv:2011.12747."},{"key":"ref38\/cit38","unstructured":"Peng, X.; Luo, S.; Guan, J.; Xie, Q.; Peng, J.; Ma, J. Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets. 2022, arXiv preprint arXiv:2205.07249."},{"key":"ref39\/cit39","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c01494"},{"key":"ref40\/cit40","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c00599"},{"key":"ref41\/cit41","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00751"},{"key":"ref42\/cit42","doi-asserted-by":"publisher","DOI":"10.1039\/c9sc01928f"},{"key":"ref43\/cit43","doi-asserted-by":"publisher","DOI":"10.1021\/ci900043r"},{"key":"ref44\/cit44","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c00305"},{"key":"ref45\/cit45","doi-asserted-by":"publisher","DOI":"10.1021\/jm030644s"},{"key":"ref46\/cit46","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkr777"},{"key":"ref47\/cit47","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-3-33"},{"key":"ref48\/cit48","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-015-0078-2"},{"key":"ref49\/cit49","unstructured":"Arora, R.; Basu, A.; Mianjy, P.; Mukherjee, A. Understanding deep neural networks with rectified linear units. 2016, arXiv preprint arXiv:1611.01491."},{"key":"ref50\/cit50","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref51\/cit51","unstructured":"Ioffe, S.; Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift.  International Conference on Machine Learning; PMLR, 2015; pp 448\u2013456."},{"key":"ref52\/cit52","unstructured":"Landrum, G. RDKit: Open-Source Cheminformatics; GitHub, 2016."},{"key":"ref53\/cit53","doi-asserted-by":"publisher","DOI":"10.1038\/353174a0"},{"key":"ref54\/cit54","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.can-13-3440"},{"key":"ref55\/cit55","doi-asserted-by":"publisher","DOI":"10.1016\/j.pharmthera.2017.02.008"},{"key":"ref56\/cit56","doi-asserted-by":"publisher","DOI":"10.1074\/jbc.ra118.004673"},{"key":"ref57\/cit57","first-page":"2825","volume":"12","author":"Pedregosa F.","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref58\/cit58","doi-asserted-by":"publisher","DOI":"10.1038\/nrd1983"},{"key":"ref59\/cit59","doi-asserted-by":"publisher","DOI":"10.1212\/01.wnl.0000095211.71092.a0"},{"key":"ref60\/cit60","doi-asserted-by":"publisher","DOI":"10.1016\/j.lfs.2005.04.029"},{"key":"ref61\/cit61","doi-asserted-by":"publisher","DOI":"10.1021\/jm010924c"},{"key":"ref62\/cit62","doi-asserted-by":"publisher","DOI":"10.1021\/jm021023m"},{"key":"ref63\/cit63","doi-asserted-by":"publisher","DOI":"10.1016\/s0014-827x(01)01024-2"}],"container-title":["Journal of Chemical Information and Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.2c00177","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.2c00177","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T21:43:59Z","timestamp":1682459039000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/doi\/10.1021\/acs.jcim.2c00177"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,6]]},"references-count":63,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2022,7,25]]}},"alternative-id":["10.1021\/acs.jcim.2c00177"],"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.2c00177","relation":{},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,6]]}}}