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Large Scale Gan Training for High Fidelity Natural Image Synthesis.\n                      Proceedings of the International Conference on Learning Representations\n                      ; New Orleans, LA, May 6\u20139, 2019."},{"key":"ref8\/cit8","unstructured":"Ramesh, A.; Pavlov, M.; Goh, G.; Gray, S.; Voss, C.; Radford, A.; Chen, M.; Sutskever, I. Zero-Shot Text-to-Image Generation.\n                      Proceedings of the 38th International Conference on Machine Learning\n                      2021, Vol. 139, pp 8821\u20138831."},{"key":"ref9\/cit9","first-page":"5998","author":"Vaswani A.","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref10\/cit10","doi-asserted-by":"publisher","unstructured":"Wu, Y.; Schuster, M.; Chen, Z.; Le, Q. V.; Norouzi, M.; Macherey, W.; Krikun, M.; Cao, Y.; Gao, Q.; Macherey, K. Google\u2019s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.\n                      arXiv\n                      , Oct. 8, 2016, ver. 2.10.48550\/arXiv.1609.08144.","DOI":"10.48550\/arXiv.1609.08144"},{"key":"ref11\/cit11","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03819-2"},{"key":"ref12\/cit12","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00751"},{"key":"ref13\/cit13","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpclett.5b00831"},{"key":"ref14\/cit14","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.108.058301"},{"key":"ref15\/cit15","doi-asserted-by":"publisher","DOI":"10.1021\/ci100050t"},{"key":"ref16\/cit16","doi-asserted-by":"publisher","DOI":"10.1021\/ci00057a005"},{"key":"ref17\/cit17","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-5-7"},{"key":"ref18\/cit18","doi-asserted-by":"publisher","DOI":"10.1007\/s10822-016-9938-8"},{"key":"ref19\/cit19","doi-asserted-by":"publisher","DOI":"10.1039\/C8SC04175J"},{"key":"ref20\/cit20","doi-asserted-by":"publisher","DOI":"10.1021\/acs.molpharmaceut.7b00346"},{"key":"ref21\/cit21","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00572"},{"key":"ref22\/cit22","doi-asserted-by":"publisher","DOI":"10.1126\/science.132.3434.1115"},{"key":"ref23\/cit23","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2014.02.012"},{"key":"ref24\/cit24","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-30839-x"},{"key":"ref25\/cit25","doi-asserted-by":"publisher","unstructured":"Bjerrum, E. J. Smiles Enumeration as Data Augmentation for Neural Network Modeling of Molecules.\n                      arXiv\n                      , May 17, 2017, ver. 2.10.48550\/arXiv.1703.07076.","DOI":"10.48550\/arXiv.1703.07076"},{"key":"ref26\/cit26","first-page":"2323","volume":"80","author":"Jin W.","year":"2018","journal-title":"Proceedings of the 35th International Conference on Machine Learning"},{"key":"ref27\/cit27","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-020-00460-5"},{"key":"ref28\/cit28","doi-asserted-by":"crossref","first-page":"85","DOI":"10.59566\/IJBS.2006.2085","volume":"2","author":"Nguyen L. A.","year":"2006","journal-title":"International journal of biomedical science: IJBS"},{"key":"ref29\/cit29","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-016-0160-4"},{"key":"ref30\/cit30","doi-asserted-by":"publisher","unstructured":"O\u2019Boyle, N.; Dalke, A. Deepsmiles: An Adaptation of Smiles for Use in Machine-Learning of Chemical Structures.\n                      ChemRxiv\n                      , Sept. 19, 2018, ver. 1.10.26434\/chemrxiv.7097960.v1","DOI":"10.26434\/chemrxiv.7097960.v1"},{"key":"ref31\/cit31","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/aba947"},{"key":"ref32\/cit32","doi-asserted-by":"publisher","DOI":"10.1002\/wcms.1603"},{"key":"ref33\/cit33","doi-asserted-by":"crossref","DOI":"10.1142\/6523","volume-title":"Bridging the Gap between Graph Edit Distance and Kernel Machines","author":"Neuhaus M.","year":"2007"},{"key":"ref34\/cit34","first-page":"1","volume":"10","author":"Gudivada V.","year":"2017","journal-title":"International Journal on Advances in Software"},{"key":"ref35\/cit35","doi-asserted-by":"publisher","DOI":"10.1021\/ci3001277"},{"key":"ref36\/cit36","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.5b00559"},{"key":"ref37\/cit37","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkw1074"},{"key":"ref38\/cit38","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gky1075"},{"key":"ref39\/cit39","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkv951"},{"key":"ref40\/cit40","doi-asserted-by":"publisher","DOI":"10.1021\/ci600423u"},{"key":"ref41\/cit41","doi-asserted-by":"publisher","DOI":"10.1002\/anie.200462457"},{"key":"ref42\/cit42","doi-asserted-by":"publisher","DOI":"10.1021\/ja902302h"},{"key":"ref43\/cit43","doi-asserted-by":"publisher","DOI":"10.1021\/ci300415d"},{"key":"ref44\/cit44","doi-asserted-by":"publisher","DOI":"10.1007\/s10822-011-9436-y"},{"key":"ref45\/cit45","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkx1037"},{"key":"ref46\/cit46","doi-asserted-by":"publisher","DOI":"10.1021\/jz200866s"},{"key":"ref47\/cit47","doi-asserted-by":"publisher","DOI":"10.1039\/C3EE42756K"},{"key":"ref48\/cit48","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2020.565644"},{"key":"ref49\/cit49","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2014.22"},{"key":"ref50\/cit50","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01288-4"},{"key":"ref51\/cit51","unstructured":"Landrum, G.\n                      Rdkit: Open-Source Cheminformatics\n                      . 2006. http:\/\/www.rdkit.org\/ (accessed Oct. 3, 2022)."},{"key":"ref52\/cit52","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-3-33"},{"key":"ref53\/cit53","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-017-0220-4"},{"key":"ref54\/cit54","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btu624"},{"key":"ref55\/cit55","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.6b00167"},{"key":"ref56\/cit56","volume-title":"Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More","author":"Ramsundar B.","year":"2019"},{"key":"ref57\/cit57","doi-asserted-by":"publisher","unstructured":"Zhu, Z.; Shi, C.; Zhang, Z.; Liu, S.; Xu, M.; Yuan, X.; Zhang, Y.; Chen, J.; Cai, H.; Lu, J. TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery.\n                      arXiv\n                      , Feb. 16, 2022, ver. 1.10.48550\/arXiv.2202.08320.","DOI":"10.48550\/arXiv.2202.08320"},{"key":"ref58\/cit58","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-3-S1-P4"},{"key":"ref59\/cit59","first-page":"8026","volume":"32","author":"Paszke A.","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref60\/cit60","unstructured":"Falcon, W.; The PyTorch lightning team\n                      Pytorch Lightning\n                      . 2019."},{"key":"ref61\/cit61","unstructured":"Abadi, M.; Barham, P.; Chen, J.; Chen, Z.; Davis, A.; Dean, J.; Devin, M.; Ghemawat, S.; Irving, G.; Isard, M. Tensorflow: A System for Large-Scale Machine Learning.\n                      Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI \u201816)\n                      ; Savannah, GA, Nov. 2\u20134, 2016; pp 265\u2013283."},{"key":"ref62\/cit62","unstructured":"Chollet, F.\n                      Keras\n                      . 2015."},{"key":"ref63\/cit63","first-page":"2825","volume":"12","author":"Pedregosa F.","year":"2011","journal-title":"The Journal of machine Learning research"},{"key":"ref64\/cit64","doi-asserted-by":"publisher","DOI":"10.1145\/1656274.1656280"},{"key":"ref65\/cit65","doi-asserted-by":"publisher","unstructured":"Graves, A. Generating Sequences with Recurrent Neural Networks.\n                      arXiv\n                      , Aug. 11, 2013, ver. 2.10.48550\/arXiv.1308.0850.","DOI":"10.48550\/arXiv.1308.0850"},{"key":"ref66\/cit66","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref67\/cit67","doi-asserted-by":"publisher","unstructured":"Cho, K.; Van Merri\u00ebnboer, B.; Gulcehre, C.; Bahdanau, D.; Bougares, F.; Schwenk, H.; Bengio, Y. Learning Phrase Representations Using Rnn Encoder-Decoder for Statistical Machine Translation.\n                      arXiv\n                      , Sept. 3, 2014, ver. 3.10.48550\/arXiv.1406.1078.","DOI":"10.48550\/arXiv.1406.1078"},{"key":"ref68\/cit68","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00512"},{"key":"ref69\/cit69","doi-asserted-by":"publisher","unstructured":"Ertl, P.; Lewis, R.; Martin, E.; Polyakov, V. In Silico Generation of Novel, Drug-Like Chemical Matter Using the Lstm Neural Network.\n                      arXiv\n                      , Dec. 20, 2017, ver. 1.10.48550\/arXiv.1712.07449.","DOI":"10.48550\/arXiv.1712.07449"},{"key":"ref70\/cit70","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-019-0328-9"},{"key":"ref71\/cit71","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-017-0235-x"},{"key":"ref72\/cit72","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-0160-y"},{"key":"ref73\/cit73","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00943"},{"key":"ref74\/cit74","doi-asserted-by":"publisher","unstructured":"Li, Y.; Vinyals, O.; Dyer, C.; Pascanu, R.; Battaglia, P. Learning Deep Generative Models of Graphs.\n                      arXiv\n                      , Mar. 8, 2018, ver. 1.10.48550\/arXiv.1803.03324.","DOI":"10.48550\/arXiv.1803.03324"},{"key":"ref75\/cit75","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-018-0287-6"},{"key":"ref76\/cit76","first-page":"5708","volume":"80","author":"You J.","year":"2018","journal-title":"Proceedings of the 35th International Conference on Machine Learning"},{"key":"ref77\/cit77","doi-asserted-by":"publisher","unstructured":"Popova, M.; Shvets, M.; Oliva, J.; Isayev, O. MolecularRNN: Generating Realistic Molecular Graphs with Optimized Properties.\n                      arXiv\n                      , May 31, 2019, ver. 1.10.48550\/arXiv.1905.13372.","DOI":"10.48550\/arXiv.1905.13372"},{"key":"ref78\/cit78","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.2c00177"},{"key":"ref79\/cit79","doi-asserted-by":"publisher","unstructured":"Kingma, D. P.; Welling, M. Auto-Encoding Variational Bayes.\n                      arXiv\n                      , 2013.10.48550\/arXiv.1312.6114.","DOI":"10.48550\/arXiv.1312.6114"},{"key":"ref80\/cit80","doi-asserted-by":"publisher","DOI":"10.1121\/1.2017061"},{"key":"ref81\/cit81","doi-asserted-by":"publisher","DOI":"10.1109\/T-C.1973.223746"},{"key":"ref82\/cit82","first-page":"1945","volume":"70","author":"Kusner M. J.","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"ref83\/cit83","unstructured":"Dai, H.; Tian, Y.; Dai, B.; Skiena, S.; Song, L. Syntax-Directed Variational Autoencoder for Structured Data.\n                      International Conference on Learning Representations\n                      , 2018."},{"key":"ref84\/cit84","volume":"31","author":"Liu Q.","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref85\/cit85","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab592"},{"key":"ref86\/cit86","first-page":"1","author":"Samanta B.","year":"2020","journal-title":"Journal of machine learning research"},{"key":"ref87\/cit87","volume":"27","author":"Goodfellow I.","year":"2014","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref88\/cit88","doi-asserted-by":"publisher","unstructured":"Kodali, N.; Abernethy, J.; Hays, J.; Kira, Z. On Convergence and Stability of Gans.\n                      arXiv\n                      , Dec. 10, 2017, ver. 5.10.48550\/arXiv.1705.07215.","DOI":"10.48550\/arXiv.1705.07215"},{"key":"ref89\/cit89","doi-asserted-by":"publisher","DOI":"10.18632\/oncotarget.14073"},{"key":"ref90\/cit90","doi-asserted-by":"publisher","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.\n                      arXiv\n                      , Feb. 7, 2018, ver. 3.10.48550\/arXiv.1705.10843.","DOI":"10.48550\/arXiv.1705.10843"},{"key":"ref91\/cit91","doi-asserted-by":"publisher","unstructured":"Sanchez-Lengeling, B.; Outeiral, C.; Guimaraes, G. L.; Aspuru-Guzik, A. Optimizing Distributions over Molecular Space. An Objective-Reinforced Generative Adversarial Network for Inverse-Design Chemistry (Organic).\n                      ChemRxiv\n                      , Aug. 17, 2017, ver. 3.10.26434\/chemrxiv.5309668.v3.","DOI":"10.26434\/chemrxiv.5309668.v3"},{"key":"ref92\/cit92","doi-asserted-by":"publisher","unstructured":"De Cao, N.; Kipf, T. MolGAN: An Implicit Generative Model for Small Molecular Graphs.\n                      arXiv\n                      , May 30, 2018, ver. 1.10.48550\/arXiv.1805.11973.","DOI":"10.48550\/arXiv.1805.11973"},{"key":"ref93\/cit93","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-019-0397-9"},{"key":"ref94\/cit94","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-019-0404-1"},{"key":"ref95\/cit95","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-022-00623-6"},{"key":"ref96\/cit96","doi-asserted-by":"publisher","DOI":"10.4310\/CMS.2010.v8.n1.a11"},{"key":"ref97\/cit97","doi-asserted-by":"publisher","DOI":"10.1002\/cpa.21423"},{"key":"ref98\/cit98","unstructured":"Dinh, L.; Krueger, D.; Bengio, Y. Nice: Non-Linear Independent Components Estimation. In\n                      International Conference on Learning Representations\n                      , 2015."},{"key":"ref99\/cit99","volume":"31","author":"Kingma D. P.","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref100\/cit100","doi-asserted-by":"publisher","unstructured":"Madhawa, K.; Ishiguro, K.; Nakago, K.; Abe, M. GraphNVP: An Invertible Flow Model for Generating Molecular Graphs.\n                      arXiv\n                      , May 28, 2019.10.48550\/arXiv.1905.11600.","DOI":"10.48550\/arXiv.1905.11600"},{"key":"ref101\/cit101","doi-asserted-by":"publisher","unstructured":"Honda, S.; Akita, H.; Ishiguro, K.; Nakanishi, T.; Oono, K. Graph Residual Flow for Molecular Graph Generation.\n                      arXiv\n                      , Sept. 30, 2019.10.48550\/arXiv.1909.13521.","DOI":"10.48550\/arXiv.1909.13521"},{"key":"ref102\/cit102","unstructured":"Behrmann, J.; Grathwohl, W.; Chen, R. T.; Duvenaud, D.; Jacobsen, J.H. Invertible Residual Networks. In\n                      International Conference on Machine Learning\n                      , 2019; PMLR: pp 573\u2013582."},{"key":"ref103\/cit103","doi-asserted-by":"publisher","unstructured":"Chen, R. T.; Behrmann, J.; Duvenaud, D.; Jacobsen, J.H. Residual Flows for Invertible Generative Modeling.\n                      arXiv\n                      , Dec. 72019, ver. 4.10.48550\/arXiv.1906.02735.","DOI":"10.48550\/arXiv.1906.02735"},{"key":"ref104\/cit104","volume":"32","author":"Song Y.","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref105\/cit105","unstructured":"Shi, C.; Xu, M.; Zhu, Z.; Zhang, W.; Zhang, M.; Tang, J. Graphaf: A Flow-Based Autoregressive Model for Molecular Graph Generation.\n                      International Conference on Learning Representations\n                      , 2020."},{"key":"ref106\/cit106","doi-asserted-by":"crossref","unstructured":"Zang, C.; Wang, F. Moflow: An Invertible Flow Model for Generating Molecular Graphs.\n                      Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining\n                      , 2020; pp 617\u2013626.","DOI":"10.1145\/3394486.3403104"},{"key":"ref107\/cit107","unstructured":"Luo, Y.; Yan, K.; Ji, S. Graphdf: A Discrete Flow Model for Molecular Graph Generation.\n                      International Conference on Machine Learning\n                      , 2021; PMLR: pp 7192\u20137203."},{"key":"ref108\/cit108","doi-asserted-by":"publisher","unstructured":"Frey, N. C.; Gadepally, V.; Ramsundar, B. Fastflows: Flow-Based Models for Molecular Graph Generation.\n                      arXiv\n                      , Jan. 28,2022.10.48550\/arXiv.2201.12419.","DOI":"10.48550\/arXiv.2201.12419"},{"key":"ref109\/cit109","doi-asserted-by":"publisher","unstructured":"Devlin, J.; Chang, M.W.; Lee, K.; Toutanova, K. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.\n                      arXiv\n                      , Oct. 11, 2018, ver. 1.10.48550\/arXiv.1810.04805.","DOI":"10.48550\/arXiv.1810.04805"},{"key":"ref110\/cit110","first-page":"1877","volume":"33","author":"Brown T.","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref111\/cit111","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-79682-4"},{"key":"ref112\/cit112","doi-asserted-by":"publisher","unstructured":"Wang, W.; Wang, Y.; Zhao, H.; Sciabola, S. A Transformer-Based Generative Model for De Novo Molecular Design.\n                      arXiv\n                      , Oct. 23, 2022, ver 2.10.48550\/arXiv.2210.08749.","DOI":"10.48550\/arXiv.2210.08749"},{"key":"ref113\/cit113","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00403-1"},{"key":"ref114\/cit114","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00639-z"},{"key":"ref115\/cit115","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad185"},{"key":"ref116\/cit116","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25662"},{"key":"ref117\/cit117","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.1c00600"},{"key":"ref118\/cit118","first-page":"6840","volume":"33","author":"Ho J.","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref119\/cit119","unstructured":"Sohl-Dickstein, J.; Weiss, E.; Maheswaranathan, N.; Ganguli, S. 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Geometric Latent Diffusion Models for 3d Molecule Generation.\n                      International Conference on Machine Learning\n                      , 2023; PMLR: pp 38592\u201338610."},{"key":"ref122\/cit122","doi-asserted-by":"crossref","first-page":"5105","DOI":"10.1609\/aaai.v37i4.25639","volume":"37","author":"Huang L.","year":"2023","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref123\/cit123","unstructured":"Guan, J.; Qian, W. W.; Peng, X.; Su, Y.; Peng, J.; Ma, J. 3d Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction.\n                      The Eleventh International Conference on Learning Representations\n                      , 2023."},{"key":"ref124\/cit124","doi-asserted-by":"publisher","unstructured":"Lin, H.; Huang, Y.; Liu, M.; Li, X.; Ji, S.; Li, S. Z. DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding.\n                      arXiv\n                      , Dec. 17, 2022, ver. 3.10.48550\/arXiv.2211.11214.","DOI":"10.48550\/arXiv.2211.11214"},{"key":"ref125\/cit125","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b01120"},{"key":"ref126\/cit126","doi-asserted-by":"publisher","unstructured":"Igashov, I.; St\u00e4rk, H.; Vignac, C.; Satorras, V. G.; Frossard, P.; Welling, M.; Bronstein, M.; Correia, B. Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design.\n                      arXiv\n                      , Oct. 11, 2022, ver 1.10.48550\/arXiv.2210.05274.","DOI":"10.48550\/arXiv.2210.05274"},{"key":"ref127\/cit127","first-page":"36533","volume":"35","author":"Wu L.","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref128\/cit128","first-page":"18872","volume":"202","author":"Lee S.","year":"2023","journal-title":"Proceedings of the 40th International Conference on Machine Learning"},{"key":"ref129\/cit129","volume":"31","author":"You J.","year":"2018","journal-title":"Advances in neural information processing systems"},{"key":"ref130\/cit130","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-47148-x"},{"key":"ref131\/cit131","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.2c00838"},{"key":"ref132\/cit132","doi-asserted-by":"publisher","unstructured":"Xu, M.; Yu, L.; Song, Y.; Shi, C.; Ermon, S.; Tang, J. Geodiff: A Geometric Diffusion Model for Molecular Conformation Generation.\n                      arXiv\n                      , Mar. 6, 2022, ver. 1.10.48550\/arXiv.2203.02923.","DOI":"10.48550\/arXiv.2203.02923"},{"key":"ref133\/cit133","first-page":"4839","volume":"119","author":"Jin W.","year":"2020","journal-title":"Proceedings of the 37th International Conference on Machine Learning"},{"key":"ref134\/cit134","doi-asserted-by":"publisher","unstructured":"Maziarz, K.; Jackson-Flux, H.; Cameron, P.; Sirockin, F.; Schneider, N.; Stiefl, N.; Segler, M.; Brockschmidt, M. Learning to Extend Molecular Scaffolds with Structural Motifs.\n                      arXiv\n                      , Dec. 14, 2021, ver. 3.10.48550\/arXiv.2103.03864.","DOI":"10.48550\/arXiv.2103.03864"},{"key":"ref135\/cit135","doi-asserted-by":"publisher","DOI":"10.1002\/advs.202206674"},{"key":"ref136\/cit136","doi-asserted-by":"publisher","unstructured":"Geng, Z.; Xie, S.; Xia, Y.; Wu, L.; Qin, T.; Wang, J.; Zhang, Y.; Wu, F.; Liu, T.Y. De Novo Molecular Generation Via Connection-Aware Motif Mining.\n                      arXiv\n                      , Feb. 26, 2023, ver. 2.10.48550\/arXiv.2302.01129.","DOI":"10.48550\/arXiv.2302.01129"},{"key":"ref137\/cit137","doi-asserted-by":"publisher","DOI":"10.1016\/S1359-6446(01)01712-3"},{"key":"ref138\/cit138","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-23720-w"},{"key":"ref139\/cit139","doi-asserted-by":"publisher","DOI":"10.1039\/C9SC01928F"},{"key":"ref140\/cit140","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2023.100678"},{"key":"ref141\/cit141","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c00599"},{"key":"ref142\/cit142","doi-asserted-by":"publisher","unstructured":"Abeer, A.; Urban, N.; Weil, M. R.; Alexander, F. J.; Yoon, B.J. Multi-Objective Latent Space Optimization of Generative Molecular Design Models.\n                      arXiv\n                      , Mar. 12022, ver. 1.10.48550\/arXiv.2203.00526.","DOI":"10.48550\/arXiv.2203.00526"},{"key":"ref143\/cit143","doi-asserted-by":"publisher","DOI":"10.1039\/D2SC00821A"},{"key":"ref144\/cit144","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-021-00561-9"},{"key":"ref145\/cit145","first-page":"4849","volume":"119","author":"Jin W.","year":"2020","journal-title":"Proceedings of the 37th International Conference on Machine Learning"},{"key":"ref146\/cit146","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jmedchem.7b00696"},{"key":"ref147\/cit147","doi-asserted-by":"publisher","unstructured":"Peng, X.; Luo, S.; Guan, J.; Xie, Q.; Peng, J.; Ma, J. Pocket2Mol: Efficient Molecular Sampling Based on 3d Protein Pockets.\n                      arXiv\n                      , May 15, 2022, ver. 1.10.48550\/arXiv.2205.07249.","DOI":"10.48550\/arXiv.2205.07249"},{"key":"ref148\/cit148","author":"Luo Y.","year":"2021","journal-title":"International Conference on Learning Representations"},{"key":"ref149\/cit149","doi-asserted-by":"publisher","unstructured":"Pang, C.; Wang, Y.; Jiang, Y.; Wang, R.; Su, R.; Wei, L. Multi-View Deep Learning Based Molecule Design and Structural Optimization Accelerates the Sars-Cov-2 Inhibitor Discovery.\n                      arXiv\n                      , Dec. 3, 2022, ver. 1.10.48550\/ARXIV.2212.01575.","DOI":"10.48550\/ARXIV.2212.01575"},{"key":"ref150\/cit150","doi-asserted-by":"crossref","unstructured":"Tan, C.; Gao, Z.; Li, S. Z. Target-Aware Molecular Graph Generation.\n                      Joint European Conference on Machine Learning and Knowledge Discovery in Databases\n                      , 2023; Springer: pp 410\u2013427.","DOI":"10.1007\/978-3-031-43427-3_25"},{"key":"ref151\/cit151","doi-asserted-by":"publisher","DOI":"10.1038\/nchem.1243"},{"key":"ref152\/cit152","doi-asserted-by":"publisher","DOI":"10.1021\/ci990307l"},{"key":"ref153\/cit153","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-1-8"},{"key":"ref154\/cit154","doi-asserted-by":"publisher","DOI":"10.1039\/c0cp00151a"},{"key":"ref155\/cit155","doi-asserted-by":"publisher","DOI":"10.1007\/s12539-019-00327-w"},{"key":"ref156\/cit156","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3147790"},{"key":"ref157\/cit157","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00839"},{"key":"ref158\/cit158","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbamcr.2019.04.004"},{"key":"ref159\/cit159","doi-asserted-by":"publisher","DOI":"10.1038\/s41587-019-0224-x"},{"key":"ref160\/cit160","doi-asserted-by":"publisher","DOI":"10.1038\/s41583-018-0093-1"},{"key":"ref161\/cit161","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-34692-w"},{"key":"ref162\/cit162","doi-asserted-by":"publisher","DOI":"10.3390\/jcm7080201"},{"key":"ref163\/cit163","unstructured":"Fultinavi\u010diu\u0305t\u0117, U.\n                      Insilico\u2019s Ai Drug Enters Phase Ii Ipf Trial\n                      . Clinical Trials Arena, 2023. https:\/\/www.clinicaltrialsarena.com\/news\/insilico-medicine-ins018055-ai\/?cf-view (accessed Sept. 5, 2023)."},{"key":"ref164\/cit164","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.2c01191"},{"key":"ref165\/cit165","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-78537-2"},{"key":"ref166\/cit166","doi-asserted-by":"publisher","unstructured":"Li, Y.; Pei, J.; Lai, L. Learning to Design Drug-Like Molecules in Three-Dimensional Space Using Deep Generative Models.\n                      arXiv\n                      , Apr. 17, 2021.10.48550\/arXiv.2104.08474.","DOI":"10.48550\/arXiv.2104.08474"},{"key":"ref167\/cit167","doi-asserted-by":"publisher","DOI":"10.3390\/molecules23051038"}],"container-title":["Journal of Chemical Information and Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.3c01496","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.3c01496","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T05:50:11Z","timestamp":1712555411000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/doi\/10.1021\/acs.jcim.3c01496"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,7]]},"references-count":167,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,4,8]]}},"alternative-id":["10.1021\/acs.jcim.3c01496"],"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.3c01496","relation":{},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,7]]}}}