{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T04:11:13Z","timestamp":1769832673113,"version":"3.49.0"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2022,1,19]],"date-time":"2022-01-19T00:00:00Z","timestamp":1642550400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["ZR2021QF023"],"award-info":[{"award-number":["ZR2021QF023"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Research and Development Project of China","award":["2021YFA1000102"],"award-info":[{"award-number":["2021YFA1000102"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["21CX06018A"],"award-info":[{"award-number":["21CX06018A"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Deep learning shortens the cycle of the drug discovery for its success in extracting features of molecules and proteins. Generating new molecules with deep learning methods could enlarge the molecule space and obtain molecules with specific properties. However, it is also a challenging task considering that the connections between atoms are constrained by chemical rules. Aiming at generating and optimizing new valid molecules, this article proposed Molecular Substructure Tree Generative Model, in which the molecule is generated by adding substructure gradually. The proposed model is based on the Variational Auto-Encoder architecture, which uses the encoder to map molecules to the latent vector space, and then builds an autoregressive generative model as a decoder to generate new molecules from Gaussian distribution. At the same time, for the molecular optimization task, a molecular optimization model based on CycleGAN was constructed. Experiments showed that the model could generate valid and novel molecules, and the optimized model effectively improves the molecular properties.<\/jats:p>","DOI":"10.1093\/bib\/bbab592","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T12:12:39Z","timestamp":1640261559000},"source":"Crossref","is-referenced-by-count":24,"title":["Molecular substructure tree generative model for de novo drug design"],"prefix":"10.1093","volume":"23","author":[{"given":"Shuang","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}]},{"given":"Tao","family":"Song","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9774-9709","authenticated-orcid":false,"given":"Shugang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China"}]},{"given":"Mingjian","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China"}]},{"given":"Zhiqiang","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5093-4221","authenticated-orcid":false,"given":"Zhen","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Qingdao University, Qingdao 266071, China"}]}],"member":"286","published-online":{"date-parts":[[2022,1,19]]},"reference":[{"key":"2022031506310223200_ref1","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/nrd3078","article-title":"How to improve R&D productivity: the pharmaceutical industry\u2019s grand challenge","volume":"9","author":"Paul","year":"2010","journal-title":"Nat Rev Drug Discov"},{"key":"2022031506310223200_ref2","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.jhealeco.2016.01.012","article-title":"Innovation in the pharmaceutical industry: new estimates of R&D costs","volume":"47","author":"DiMasi","year":"2016","journal-title":"J Health Econ"},{"key":"2022031506310223200_ref3","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1007\/s10822-013-9672-4","article-title":"Estimation of the size of drug-like chemical space based on GDB-17 data","volume":"27","author":"Polishchuk","year":"2013","journal-title":"J Comput Aided Mol Des"},{"key":"2022031506310223200_ref4","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1007\/978-1-60761-839-3_12","article-title":"De novo drug design","volume":"672","author":"Hartenfeller","year":"2010","journal-title":"Chemoinformatics Comput Chem Biol"},{"key":"2022031506310223200_ref5","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.2174\/1381612827666210129123231","article-title":"Recent progress of deep learning in drug discovery","volume":"27","author":"Wang","year":"2021","journal-title":"Curr Pharm Des"},{"key":"2022031506310223200_ref6","doi-asserted-by":"crossref","first-page":"606668","DOI":"10.3389\/fphar.2020.606668","article-title":"Improvement of prediction performance with conjoint molecular fingerprint in deep learning","volume":"11","author":"Xie","year":"2020","journal-title":"Front Pharmacol"},{"key":"2022031506310223200_ref7","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab078","article-title":"A spatial-temporal gated attention module for molecular property prediction based on molecular geometry","volume":"22","author":"Li","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022031506310223200_ref8","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1111\/cbdd.13648","article-title":"Multitask deep networks with grid featurization achieve improved scoring performance for protein--ligand binding","volume":"96","author":"Xie","year":"2020","journal-title":"Chem Biol Drug Des"},{"key":"2022031506310223200_ref9","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.neucom.2020.12.068","article-title":"Prediction of drug-target interactions based on multi-layer network representation learning","volume":"434","author":"Shang","year":"2021","journal-title":"Neurocomputing"},{"key":"2022031506310223200_ref10","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.3390\/biom11081119","article-title":"MCN-CPI: multiscale convolutional network for compound--protein interaction prediction","volume":"11","author":"Wang","year":"2021","journal-title":"Biomolecules"},{"key":"2022031506310223200_ref11","doi-asserted-by":"crossref","first-page":"8993","DOI":"10.3390\/ijms22168993","article-title":"SAG-DTA: prediction of drug\u2013target affinity using self-attention graph network","volume":"22","author":"Zhang","year":"2021","journal-title":"Int J Mol Sci"},{"key":"2022031506310223200_ref12","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab319","article-title":"Drug repositioning based on the heterogeneous information fusion graph convolutional network","volume":"22","author":"Cai","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022031506310223200_ref13","first-page":"2306","article-title":"Repositioning molecules of Chinese medicine to targets of SARS-Cov-2 by deep learning method","volume-title":"BIBM","author":"Song","year":"2020"},{"key":"2022031506310223200_ref14","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbab344","article-title":"Molecular design in drug discovery: a comprehensive review of deep generative models","volume":"22","author":"Cheng","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022031506310223200_ref15","article-title":"Improving de novo molecule generation by embedding LSTM and attention mechanism in CycleGAN","volume":"12","author":"Wang","year":"2021","journal-title":"Front Genet"},{"key":"2022031506310223200_ref16","first-page":"2672","article-title":"Generative adversarial nets","volume":"2","author":"Goodfellow","year":"2014","journal-title":"Adv Neural Inf Process Syst"},{"key":"2022031506310223200_ref17","first-page":"1","article-title":"Auto-encoding variational Bayes","volume":"1050","author":"Kingma","year":"2014","journal-title":"Stat"},{"key":"2022031506310223200_ref18","article-title":"Deep captioning with multimodal recurrent neural networks (m-rnn)","author":"Mao","journal-title":"arXiv"},{"key":"2022031506310223200_ref19","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1021\/acscentsci.7b00512","article-title":"Generating focused molecule libraries for drug discovery with recurrent neural networks","volume":"4","author":"Segler","year":"2017","journal-title":"ACS Cent Sci"},{"key":"2022031506310223200_ref20","doi-asserted-by":"crossref","first-page":"1700111","DOI":"10.1002\/minf.201700111","article-title":"Generative recurrent networks for de novo drug design","volume":"37","author":"Gupta","year":"2018","journal-title":"Mol Inform"},{"key":"2022031506310223200_ref21","doi-asserted-by":"crossref","first-page":"3098","DOI":"10.1021\/acs.molpharmaceut.7b00346","article-title":"druGAN: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico","volume":"14","author":"Kadurin","year":"2017","journal-title":"Mol Pharm"},{"key":"2022031506310223200_ref22","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","article-title":"Automatic chemical design using a data-driven continuous representation of molecules","volume":"4","author":"G\u00f3mez-Bombarelli","year":"2018","journal-title":"ACS Cent Sci"},{"key":"2022031506310223200_ref23","doi-asserted-by":"crossref","first-page":"4386","DOI":"10.1021\/acs.molpharmaceut.7b01137","article-title":"Adversarial threshold neural computer for molecular de novo design","volume":"15","author":"Putin","year":"2018","journal-title":"Mol Pharm"},{"key":"2022031506310223200_ref24","first-page":"1","article-title":"Molecular generative model based on conditional variational autoencoder for de novo molecular design","volume":"10","author":"Lim","year":"2018","journal-title":"J Chem"},{"key":"2022031506310223200_ref25","first-page":"1945","article-title":"Grammar variational autoencoder","volume":"70","author":"Kusner","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"2022031506310223200_ref26","article-title":"Syntax-directed variational autoencoder for structured data","author":"Dai","year":"2018","journal-title":"ICLR"},{"key":"2022031506310223200_ref27","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3390\/biom8040131","article-title":"Improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders","volume":"8","author":"Bjerrum","year":"2018","journal-title":"Biomolecules"},{"key":"2022031506310223200_ref28","doi-asserted-by":"crossref","first-page":"213","DOI":"10.2174\/138620706776055539","article-title":"Computational methods in developing quantitative structure-activity relationships (QSAR): a review","volume":"9","author":"Dudek","year":"2006","journal-title":"Comb Chem High Throughput Screen"},{"key":"2022031506310223200_ref29","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1126\/science.aat2663","article-title":"Inverse molecular design using machine learning: generative models for matter engineering","volume":"361","author":"Sanchez-Lengeling","year":"2018","journal-title":"Science (80-)"},{"key":"2022031506310223200_ref30","article-title":"MolGAN: an implicit generative model for small molecular graphs","author":"De Cao","journal-title":"arXiv"},{"key":"2022031506310223200_ref31","first-page":"412","article-title":"Graphvae: towards generation of small graphs using variational autoencoders","volume":"27","author":"Simonovsky","year":"2018","journal-title":"Int Conf Artif Neural Networks"},{"key":"2022031506310223200_ref32","article-title":"Towards interpretable sparse graph representation learning with laplacian pooling","author":"Noutahi","journal-title":"arXiv"},{"key":"2022031506310223200_ref33","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/s13321-019-0396-x","article-title":"Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation","volume":"11","author":"Kwon","year":"2019","journal-title":"J Chem"},{"key":"2022031506310223200_ref34","article-title":"Learning deep generative models of graphs","author":"Li","journal-title":"arXiv"},{"key":"2022031506310223200_ref35","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s13321-018-0287-6","article-title":"Multi-objective de novo drug design with conditional graph generative model","volume":"10","author":"Li","year":"2018","journal-title":"J Chem"},{"key":"2022031506310223200_ref36","first-page":"6410","article-title":"Graph convolutional policy network for goal-directed molecular graph generation","volume":"31","author":"You","year":"2018","journal-title":"Adv Neural Inf Process Syst"},{"key":"2022031506310223200_ref37","first-page":"1110","article-title":"Nevae: a deep generative model for molecular graphs","volume":"33","author":"Samanta","year":"2019","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"2022031506310223200_ref38","article-title":"Defactor: differentiable edge factorization-based probabilistic graph generation","author":"Assouel","year":"2018","journal-title":"arXiv"},{"key":"2022031506310223200_ref39","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1039\/C9SC04503A","article-title":"Scaffold-based molecular design with a graph generative model","volume":"11","author":"Lim","year":"2020","journal-title":"Chem Sci"},{"key":"2022031506310223200_ref40","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1186\/s13321-017-0225-z","article-title":"An algorithm to identify functional groups in organic molecules","volume":"9","author":"Ertl","year":"2017","journal-title":"J Chem"},{"key":"2022031506310223200_ref41","first-page":"638","article-title":"Core: automatic molecule optimization using copy & refine strategy","volume":"34","author":"Fu","year":"2020","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"2022031506310223200_ref42","first-page":"2323","article-title":"Junction tree variational autoencoder for molecular graph generation","volume":"35","author":"Jin","year":"2018","journal-title":"ICML"},{"key":"2022031506310223200_ref43","first-page":"5708","article-title":"GraphRNN\u00a0: generating realistic graphs with deep auto-regressive models","volume":"35","author":"You","year":"2018","journal-title":"ICML"},{"key":"2022031506310223200_ref44","doi-asserted-by":"crossref","first-page":"18601","DOI":"10.1109\/ACCESS.2020.2968535","article-title":"Molecular property prediction based on a multichannel substructure graph","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"2022031506310223200_ref45","volume-title":"Lists, Decisions and Graphs","author":"Bender","year":"2010"},{"key":"2022031506310223200_ref46","first-page":"1","article-title":"Mol-CycleGAN: a generative model for molecular optimization","volume":"12","author":"Maziarka","year":"2020","journal-title":"J Chem"},{"key":"2022031506310223200_ref47","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1021\/ci3001277","article-title":"ZINC: a free tool to discover chemistry for biology","volume":"52","author":"Irwin","year":"2012","journal-title":"J Chem Inf Model"},{"key":"2022031506310223200_ref48","article-title":"Graphnvp: an invertible flow model for generating molecular graphs","author":"Madhawa","journal-title":"arXiv"},{"key":"2022031506310223200_ref49","article-title":"MolecularRNN: generating realistic molecular graphs with optimized properties","author":"Popova","journal-title":"arXiv"},{"key":"2022031506310223200_ref50","article-title":"GraphAF: a flow-based autoregressive model for molecular graph generation","author":"Shi","year":"2020","journal-title":"ICLR"},{"key":"2022031506310223200_ref51","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa364","article-title":"Deep inverse reinforcement learning for structural evolution of small molecules","volume":"22","author":"Agyemang","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022031506310223200_ref52","first-page":"1","article-title":"Molecular de-novo design through deep reinforcement learning","volume":"9","author":"Olivecrona","year":"2017","journal-title":"J Chem"},{"key":"2022031506310223200_ref53","doi-asserted-by":"crossref","DOI":"10.1126\/sciadv.aap7885","article-title":"Deep reinforcement learning for de novo drug design","volume":"4","author":"Popova","year":"2018","journal-title":"Sci Adv"},{"key":"2022031506310223200_ref54","first-page":"1","article-title":"ExCAPE-DB: an integrated large scale dataset facilitating big data analysis in chemogenomics","volume":"9","author":"Sun","year":"2017","journal-title":"J Chem"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/2\/bbab592\/42806100\/bbab592.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/2\/bbab592\/42806100\/bbab592.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T06:40:03Z","timestamp":1647326403000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab592\/6510156"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,19]]},"references-count":54,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,3,10]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab592","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,3]]},"published":{"date-parts":[[2022,1,19]]},"article-number":"bbab592"}}