{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T03:59:26Z","timestamp":1772423966169,"version":"3.50.1"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T00:00:00Z","timestamp":1675296000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T00:00:00Z","timestamp":1675296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"ENGIE"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>In this work, we provide further development of the junction tree variational autoencoder (JT VAE) architecture in terms of implementation and application of the internal feature space of the model. Pretraining of JT VAE on a large dataset and further optimization with a regression model led to a latent space that can solve several tasks simultaneously: prediction, generation, and optimization. We use the ZINC database as a source of molecules for the JT VAE pretraining and the QM9 dataset with its HOMO values to show the application case. We evaluate our model on multiple tasks such as property (value) prediction, generation of new molecules with predefined properties, and structure modification toward the property. Across these tasks, our model shows improvements in generation and optimization tasks while preserving the precision of state-of-the-art models.<\/jats:p>","DOI":"10.1186\/s13321-023-00681-4","type":"journal-article","created":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T04:09:08Z","timestamp":1675310948000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Generative model based on junction tree variational autoencoder for HOMO value prediction and molecular optimization"],"prefix":"10.1186","volume":"15","author":[{"given":"Vladimir","family":"Kondratyev","sequence":"first","affiliation":[]},{"given":"Marian","family":"Dryzhakov","sequence":"additional","affiliation":[]},{"given":"Timur","family":"Gimadiev","sequence":"additional","affiliation":[]},{"given":"Dmitriy","family":"Slutskiy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,2,2]]},"reference":[{"key":"681_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2021.100379","volume":"40","author":"S Dong","year":"2021","unstructured":"Dong S, Wang P, Abbas K (2021) A survey on deep learning and its applications. Comput Sci Rev 40:100379. https:\/\/doi.org\/10.1016\/j.cosrev.2021.100379","journal-title":"Comput Sci Rev"},{"key":"681_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/s12039-021-01995-2","author":"A Karthikeyan","year":"2022","unstructured":"Karthikeyan A, Priyakumar U (2022) Artificial intelligence: machine learning for chemical sciences. J Chem Sci. https:\/\/doi.org\/10.1007\/s12039-021-01995-2","journal-title":"J Chem Sci"},{"key":"681_CR3","doi-asserted-by":"publisher","unstructured":"Hedderich MA, Lange L, Adel H, Str\u00f6tgen J, Klakow D (2021) A survey on recent approaches for natural language processing in low-resource scenarios. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 2545\u2013 2568. Association for Computational Linguistics, Online. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.201. https:\/\/aclanthology.org\/2021.naacl-main.201https:\/\/aclanthology.org\/2021.naacl-main.201","DOI":"10.18653\/v1\/2021.naacl-main.201"},{"issue":"1","key":"681_CR4","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2021","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Yu PS (2021) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4\u201324. https:\/\/doi.org\/10.1109\/TNNLS.2020.2978386","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"681_CR5","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1021\/ci00057a005","volume":"28","author":"D Weininger","year":"1998","unstructured":"Weininger D (1998) Smiles, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Comput Sci 28(1):31\u201336. https:\/\/doi.org\/10.1021\/ci00057a005","journal-title":"J Chem Inf Comput Sci"},{"issue":"10","key":"681_CR6","doi-asserted-by":"publisher","first-page":"2185","DOI":"10.1016\/j.orgel.2012.06.015","volume":"13","author":"MY Jo","year":"2012","unstructured":"Jo MY, Park SJ, Park T, Won YS, Kim JH (2012) Relationship between homo energy level and open circuit voltage of polymer solar cells. Org Electron 13(10):2185\u20132191. https:\/\/doi.org\/10.1016\/j.orgel.2012.06.015","journal-title":"Org Electron"},{"issue":"10","key":"681_CR7","doi-asserted-by":"publisher","first-page":"2553","DOI":"10.32604\/jrm.2022.020967","volume":"10","author":"DDY Setsoafia","year":"2022","unstructured":"Setsoafia DDY, Ram KS, Mehdizadeh-Rad H, Ompong D, Murthy V, Singhs J (2022) Dft and td-dft calculations of orbital energies and photovoltaic properties of small molecule donor and acceptor materials used in organic solar cells. J Renew Mater 10(10):2553\u20132567. https:\/\/doi.org\/10.32604\/jrm.2022.020967","journal-title":"J Renew Mater"},{"key":"681_CR8","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-019-0391-2","author":"M Glavatskikh","year":"2019","unstructured":"Glavatskikh M, Leguy J, Hunault G, Cauchy T, Da Mota B (2019) Dataset\u2019s chemical diversity limits the generalizability of machine learning predictions. J Cheminform. https:\/\/doi.org\/10.1186\/s13321-019-0391-2","journal-title":"J Cheminform"},{"key":"681_CR9","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.108.058301","volume":"108","author":"M Rupp","year":"2012","unstructured":"Rupp M, Tkatchenko A, M\u00fcller K-R, von Lilienfeld OA (2012) Fast and accurate modeling of molecular atomization energies with machine learning. Phys Rev Lett 108:058301. https:\/\/doi.org\/10.1103\/PhysRevLett.108.058301","journal-title":"Phys Rev Lett"},{"issue":"8","key":"681_CR10","doi-asserted-by":"publisher","first-page":"3404","DOI":"10.1021\/ct400195d","volume":"9","author":"K Hansen","year":"2013","unstructured":"Hansen K, Montavon G, Biegler F, Fazli S, Rupp M, Scheffler M, von Lilienfeld OA, Tkatchenko A, M\u00fcller K-R (2013) Assessment and validation of machine learning methods for predicting molecular atomization energies. J Chem Theory Comput 9(8):3404\u20133419. https:\/\/doi.org\/10.1021\/ct400195d","journal-title":"J Chem Theory Comput"},{"issue":"12","key":"681_CR11","doi-asserted-by":"publisher","first-page":"2326","DOI":"10.1021\/acs.jpclett.5b00831","volume":"6","author":"K Hansen","year":"2015","unstructured":"Hansen K, Biegler F, Ramakrishnan R, Pronobis W, von Lilienfeld OA, M\u00fcller K-R, Tkatchenko A (2015) Machine learning predictions of molecular properties: accurate many-body potentials and nonlocality in chemical space. J Phys Chem Lett 6(12):2326\u20132331. https:\/\/doi.org\/10.1021\/acs.jpclett.5b00831","journal-title":"J Phys Chem Lett"},{"key":"681_CR12","unstructured":"Ramakrishnan\u00a0R (2015)  v.L.O.: Many molecular properties from one kernel in chemical space. Chimia (Aarau)"},{"issue":"16","key":"681_CR13","doi-asserted-by":"publisher","DOI":"10.1063\/1.4964627","volume":"145","author":"B Huang","year":"2016","unstructured":"Huang B, von Lilienfeld OA (2016) Communication: Understanding molecular representations in machine learning: the role of uniqueness and target similarity. J Chem Phys 145(16):161102. https:\/\/doi.org\/10.1063\/1.4964627","journal-title":"J Chem Phys"},{"issue":"11","key":"681_CR14","doi-asserted-by":"publisher","first-page":"5255","DOI":"10.1021\/acs.jctc.7b00577","volume":"13","author":"FA Faber","year":"2017","unstructured":"Faber FA, Hutchison L, Huang B, Gilmer J, Schoenholz SS, Dahl GE, Vinyals O, Kearnes S, Riley PF, von Lilienfeld OA (2017) Prediction errors of molecular machine learning models lower than hybrid dft error. J Chem Theory Comput 13(11):5255\u20135264. https:\/\/doi.org\/10.1021\/acs.jctc.7b00577","journal-title":"J Chem Theory Comput"},{"issue":"1063\/1","key":"681_CR15","doi-asserted-by":"publisher","first-page":"5020441","DOI":"10.1063\/1.5020441","volume":"10","author":"CR Collins","year":"2018","unstructured":"Collins CR, Loyd Gordon GJ (2018) Constant size descriptors for accurate machine learning models of molecular properties. J Chem Phys 10(1063\/1):5020441. https:\/\/doi.org\/10.1063\/1.5020441","journal-title":"J Chem Phys"},{"issue":"12","key":"681_CR16","doi-asserted-by":"publisher","first-page":"1701816","DOI":"10.1126\/sciadv.1701816","volume":"3","author":"AP Bart\u00f3k","year":"2017","unstructured":"Bart\u00f3k AP, De S, Poelking C, Bernstein N, Kermode JR, Cs\u00e1nyi G, Ceriotti M (2017) Machine learning unifies the modeling of materials and molecules. Sci Adv 3(12):1701816. https:\/\/doi.org\/10.1126\/sciadv.1701816","journal-title":"Sci Adv"},{"issue":"9","key":"681_CR17","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/15\/9\/095003","volume":"15","author":"G Montavon","year":"2013","unstructured":"Montavon G, Rupp M, Gobre V, Vazquez-Mayagoitia A, Hansen K, Tkatchenko A, M\u00fcller K-R, von Lilienfeld OA (2013) Machine learning of molecular electronic properties in chemical compound space. New J Phys 15(9):095003. https:\/\/doi.org\/10.1088\/1367-2630\/15\/9\/095003","journal-title":"New J Phys"},{"issue":"6","key":"681_CR18","doi-asserted-by":"publisher","first-page":"3678","DOI":"10.1021\/acs.jctc.9b00181","volume":"15","author":"OT Unke","year":"2019","unstructured":"Unke OT, Meuwly M (2019) Physnet: a neural network for predicting energies, forces, dipole moments, and partial charges. J Chem Theory Comput 15(6):3678\u20133693. https:\/\/doi.org\/10.1021\/acs.jctc.9b00181","journal-title":"J Chem Theory Comput"},{"key":"681_CR19","doi-asserted-by":"publisher","first-page":"3192","DOI":"10.1039\/C6SC05720A","volume":"8","author":"JS Smith","year":"2017","unstructured":"Smith JS, Isayev O, Roitberg AE (2017) Ani-1: an extensible neural network potential with dft accuracy at force field computational cost. Chem Sci 8:3192\u20133203. https:\/\/doi.org\/10.1039\/C6SC05720A","journal-title":"Chem Sci"},{"issue":"1","key":"681_CR20","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1021\/acs.jcim.6b00340","volume":"57","author":"F Pereira","year":"2017","unstructured":"Pereira F, Xiao K, Latino DARS, Wu C, Zhang Q, Aires-de-Sousa J (2017) Machine learning methods to predict density functional theory b3lyp energies of homo and lumo orbitals. J Chem Inf Model 57(1):11\u201321. https:\/\/doi.org\/10.1021\/acs.jcim.6b00340","journal-title":"J Chem Inf Model"},{"key":"681_CR21","unstructured":"Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: Proceedings of the 34th International Conference on Machine Learning, Vol 70, pp. 1263\u2013 1272. JMLR.org"},{"key":"681_CR22","unstructured":"Sch\u00fctt KT, Kindermans P-J, Sauceda HE, Chmiela S, Tkatchenko A, M\u00fcller K-R (2017) Schnet: a continuous-filter convolutional neural network for modeling quantum interactions. Adv Neural Inf Process Syst 30:992\u20131002.https:\/\/doi.org\/10.48550\/ARXIV.1706.08566"},{"issue":"24","key":"681_CR23","doi-asserted-by":"publisher","DOI":"10.1063\/1.5024797","volume":"148","author":"TS Hy","year":"2018","unstructured":"Hy TS, Trivedi S, Pan H, Anderson BM, Kondor R (2018) Predicting molecular properties with covariant compositional networks. J Chem Phys 148(24):241745. https:\/\/doi.org\/10.1063\/1.5024797","journal-title":"J Chem Phys"},{"key":"681_CR24","doi-asserted-by":"publisher","unstructured":"Hy TS, Trivedi S, Pan H, Anderson BM, Kondor R, Hou F, Wu Z, Hu Z, Xiao Z, Wang l, Zhang X, Li G (2018) comparison study on the prediction of multiple molecular properties by various neural networks. J Chem Phys. https:\/\/doi.org\/10.1021\/acs.jpca.8b09376","DOI":"10.1021\/acs.jpca.8b09376"},{"issue":"24","key":"681_CR25","doi-asserted-by":"publisher","DOI":"10.1063\/1.5011181","volume":"148","author":"N Lubbers","year":"2018","unstructured":"Lubbers N, Smith JS, Barros K (2018) Hierarchical modeling of molecular energies using a deep neural network. J Chem Phys 148(24):241715. https:\/\/doi.org\/10.1063\/1.5011181","journal-title":"J Chem Phys"},{"issue":"24","key":"681_CR26","doi-asserted-by":"publisher","DOI":"10.1063\/1.5017898","volume":"148","author":"OT Unke","year":"2018","unstructured":"Unke OT, Meuwly M (2018) A reactive, scalable, and transferable model for molecular energies from a neural network approach based on local information. J Chem Phys 148(24):241708. https:\/\/doi.org\/10.1063\/1.5017898","journal-title":"J Chem Phys"},{"key":"681_CR27","unstructured":"Jin W, Barzilay R, Jaakkola T (2019) Junction tree variational autoencoder for molecular graph generation 1802:04364"},{"issue":"2","key":"681_CR28","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","volume":"4","author":"R G\u00f3mez-Bombarelli","year":"2018","unstructured":"G\u00f3mez-Bombarelli R, Wei JN, Duvenaud D, Hern\u00e1ndez-Lobato JM, S\u00e1nchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel TD, Adams RP, Aspuru-Guzik A (2018) Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci 4(2):268\u2013276. https:\/\/doi.org\/10.1021\/acscentsci.7b00572","journal-title":"ACS Cent Sci"},{"key":"681_CR29","unstructured":"Kingma DP, Welling M (2014) Auto-encoding variational bayes. In: 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14\u201316, 2014, Conference Track Proceedings. arXiv:http:\/\/arxiv.org\/abs\/1312.6114v10"},{"key":"681_CR30","unstructured":"Kusner MJ, Paige B, Hern\u00e1ndez-Lobato JM (2017) Grammar variational autoencoder. In: Proceedings of the 34th International Conference on Machine Learning\u2014Volume 70. ICML\u201917, pp. 1945\u2013 1954. JMLR.org"},{"key":"681_CR31","doi-asserted-by":"crossref","unstructured":"Ramakrishnan R, Dral PO, Rupp M, von Lilienfeld OA (2014) Quantum chemistry structures and properties of 134 kilo molecules. Sci Data 1","DOI":"10.1038\/sdata.2014.22"},{"issue":"7","key":"681_CR32","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1021\/ci3001277","volume":"52","author":"JJ Irwin","year":"2012","unstructured":"Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG (2012) Zinc: a free tool to discover chemistry for biology. J Chem Inf Model 52(7):1757\u20131768. https:\/\/doi.org\/10.1021\/ci3001277","journal-title":"J Chem Inf Model"},{"key":"681_CR33","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s00894-003-0143-z","volume":"10","author":"DPW Margetic\u00a0D","year":"2004","unstructured":"Margetic\u00a0D DPW, Warrener RN (2004) Diels-alder reactivity of benzannulated isobenzofurans as assessed by density functional theory. J Mol Model 10:87\u201393. https:\/\/doi.org\/10.1007\/s00894-003-0143-z","journal-title":"J Mol Model"},{"key":"681_CR34","unstructured":"De\u00a0Cao N, Kipf T (2018) MolGAN: an implicit generative model for small molecular graphs. arXiv: 1805.11973"},{"key":"681_CR35","doi-asserted-by":"crossref","unstructured":"\u0141ukasz Maziarka Pocha A, Kaczmarczyk J, Warcho\u0142 M(2019) Mol-CycleGAN\u2014a generative model for molecular optimization (2019). https:\/\/openreview.net\/forum?id=BklKFo09YX","DOI":"10.1007\/978-3-030-30493-5_77"},{"key":"681_CR36","unstructured":"Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In: Precup D, Teh YW (eds) Proceedings of the 34th International conference on machine learning. Proceedings of Machine Learning Research, vol. 70, pp. 214\u2013223. PMLR. https:\/\/proceedings.mlr.press\/v70\/arjovsky17a.html"},{"key":"681_CR37","unstructured":"Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC ( 2017) Improved training of wasserstein gans. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, Garnett R (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper\/2017\/file\/892c3b1c6dccd52936e27cbd0ff683d6-Paper.pdf"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-023-00681-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-023-00681-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-023-00681-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T04:22:38Z","timestamp":1675311758000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-023-00681-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,2]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["681"],"URL":"https:\/\/doi.org\/10.1186\/s13321-023-00681-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv-2022-z1l07","asserted-by":"object"}]},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,2]]},"assertion":[{"value":"22 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"11"}}