{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T14:45:06Z","timestamp":1758120306593,"version":"3.40.3"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030726980"},{"type":"electronic","value":"9783030726997"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-72699-7_6","type":"book-chapter","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T15:03:24Z","timestamp":1617203004000},"page":"81-96","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Combining Multi-objective Evolutionary Algorithms with Deep Generative Models Towards Focused Molecular Design"],"prefix":"10.1007","author":[{"given":"Tiago","family":"Sousa","sequence":"first","affiliation":[]},{"given":"Jo\u00e3o","family":"Correia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8661-9626","authenticated-orcid":false,"given":"Vitor","family":"Pereira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8439-8172","authenticated-orcid":false,"given":"Miguel","family":"Rocha","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,1]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","first-page":"100598","DOI":"10.1016\/j.swevo.2019.100598","volume":"51","author":"A Ben\u00edtez-Hidalgo","year":"2019","unstructured":"Ben\u00edtez-Hidalgo, A., Nebro, A.J., Garc\u00eda-Nieto, J., Oregi, I., Ser, J.D.: jmetalpy: a python framework for multi-objective optimization with metaheuristics. Swarm Evol. Comput. 51, 100598 (2019)","journal-title":"Swarm Evol. Comput."},{"issue":"2","key":"6_CR2","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1038\/nchem.1243","volume":"4","author":"GR Bickerton","year":"2012","unstructured":"Bickerton, G.R., Paolini, G.V., Besnard, J., Muresan, S., Hopkins, A.L.: Quantifying the chemical beauty of drugs. Nature Chem. 4(2), 90\u201398 (2012)","journal-title":"Nature Chem."},{"key":"6_CR3","unstructured":"Bresson, X., Laurent, T.: A Two-Step Graph Convolutional Decoder for Molecule Generation. arXiv:1906.03412 [cs, stat] (2019)"},{"issue":"3","key":"6_CR4","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1021\/ci034290p","volume":"44","author":"N Brown","year":"2004","unstructured":"Brown, N., McKay, B., Gilardoni, F., Gasteiger, J.: A graph-based genetic algorithm and its application to the multiobjective evolution of median molecules. J. Chem. Inf. Comput. Sci. 44(3), 1079\u20131087 (2004)","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"6_CR5","unstructured":"Dai, H., Tian, Y., Dai, B., Skiena, S., Song, L.: Syntax-directed variational autoencoder for structured data. arXiv preprint arXiv:1802.08786 (2018)"},{"issue":"4","key":"6_CR6","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TEVC.2013.2281535","volume":"18","author":"K Deb","year":"2014","unstructured":"Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: Solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577\u2013601 (2014)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"6_CR7","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1016\/j.asoc.2014.09.042","volume":"27","author":"RV Devi","year":"2015","unstructured":"Devi, R.V., Sathya, S.S., Coumar, M.S.: Evolutionary algorithms for de novo drug design - a survey. Appl. Soft. Comput. 27, 543\u2013552 (2015)","journal-title":"Appl. Soft. Comput."},{"key":"6_CR8","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.jhealeco.2016.01.012","volume":"47","author":"JA DiMasi","year":"2016","unstructured":"DiMasi, J.A., Grabowski, H.G., Hansen, R.W.: Innovation in the pharmaceutical industry: new estimates of R&D costs. J. Health Econ. 47, 20\u201333 (2016)","journal-title":"J. Health Econ."},{"key":"6_CR9","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"issue":"2","key":"6_CR10","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1039\/C9SC04026A","volume":"11","author":"RR Griffiths","year":"2020","unstructured":"Griffiths, R.R., Hern\u00e1ndez-Lobato, J.M.: Constrained Bayesian optimization for automatic chemical design using variational autoencoders. Chem. Sci. 11(2), 577\u2013586 (2020)","journal-title":"Chem. Sci."},{"key":"6_CR11","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. arXiv:1705.10843 (2017)"},{"issue":"2","key":"6_CR12","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., et al.: Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Sci. 4(2), 268\u2013276 (2018)","journal-title":"ACS Central Sci."},{"key":"6_CR13","doi-asserted-by":"publisher","first-page":"85","DOI":"10.3389\/fenvs.2015.00085","volume":"3","author":"R Huang","year":"2016","unstructured":"Huang, R., et al.: Tox21challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs. Frontiers Environ. Sci. 3, 85 (2016)","journal-title":"Frontiers Environ. Sci."},{"key":"6_CR14","unstructured":"Jin, W., Barzilay, R., Jaakkola, T.: Junction tree variational autoencoder for molecular graph generation. In: International Conference on Machine Learning. pp. 2323\u20132332. PMLR (2018)"},{"key":"6_CR15","unstructured":"Jin, W., Yang, K., Barzilay, R., Jaakkola, T.: Learning multimodal graph-to-graph translation for molecular optimization. arXiv:1812.01070 [cs, stat] (2019)"},{"key":"6_CR16","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Kukkonen, S., Lampinen, J.: Gde3: the third evolution step of generalized differential evolution. In: 2005 IEEE Congress on Evolutionary Computation. vol. 1, pp. 443\u2013450 (2005)","DOI":"10.1109\/CEC.2005.1554717"},{"key":"6_CR18","unstructured":"Kusner, M.J., Paige, B., Hern\u00e1ndez-Lobato, J.M.: Grammar variational autoencoder. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1945\u20131954. JMLR. org (2017)"},{"key":"6_CR19","unstructured":"Landrum, G.: Rdkit: open-source cheminformatics software (2016)"},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Leguy, J., Cauchy, T., Glavatskikh, M., Duval, B., Da\u00a0Mota, B.: Evomol: aflexible and interpretable evolutionary algorithm for unbiased de novomolecular generation. Cheminform 12(55) (2020)","DOI":"10.1186\/s13321-020-00458-z"},{"key":"6_CR21","unstructured":"Liu, Q., Allamanis, M., Brockschmidt, M., Gaunt, A.: Constrained graph variational autoencoders for molecule design. In: Advances in Neural Information Processing Systems, pp. 7795\u20137804 (2018)"},{"key":"6_CR22","unstructured":"Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I.: Adversarial autoencoders. In: International Conference on Learning Representations (2016). http:\/\/arxiv.org\/abs\/1511.05644"},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Marim, L., Lemes, M., Dal Pino\u00a0Jr, A.: Neural-network-assisted genetic algorithm applied to silicon clusters. Phys. Rev. A 67, 033203 (2003)","DOI":"10.1103\/PhysRevA.67.033203"},{"issue":"1","key":"6_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-019-0407-y","volume":"12","author":"\u0141 Maziarka","year":"2020","unstructured":"Maziarka, \u0141., Pocha, A., Kaczmarczyk, J., Rataj, K., Danel, T., Warcho\u0142, M.: Mol-cyclegan: a generative model for molecular optimization. J. Chem. 12(1), 1\u201318 (2020)","journal-title":"J. Chem."},{"key":"6_CR25","doi-asserted-by":"crossref","unstructured":"Olivecrona, M., Blaschke, T., Engkvist, O., Chen, H.: Molecular de novo design through deep reinforcement learning (2017)","DOI":"10.1186\/s13321-017-0235-x"},{"key":"6_CR26","unstructured":"Paszke, A., et al.: Pytorch: An imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 8024\u20138035. Curran Associates, Inc. (2019)"},{"key":"6_CR27","doi-asserted-by":"crossref","unstructured":"Patra, T.K., Meenakshisundaram, V., Hung, J.H., Simmons, D.S.: Neural-network-biased genetic algorithms for materials design: Evolutionary algorithms that learn (2017)","DOI":"10.1021\/acscombsci.6b00136"},{"issue":"8","key":"6_CR28","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1007\/s10822-013-9672-4","volume":"27","author":"PG Polishchuk","year":"2013","unstructured":"Polishchuk, P.G., et al.: Estimation of the size of drug-like chemical space based on GDB-17 data. J. Comput.-Aided Mol. Des. 27(8), 675\u2013679 (2013)","journal-title":"J. Comput.-Aided Mol. Des."},{"key":"6_CR29","doi-asserted-by":"publisher","first-page":"565644","DOI":"10.3389\/fphar.2020.565644","volume":"11","author":"D Polykovskiy","year":"2020","unstructured":"Polykovskiy, D., et al.: Molecular sets (MOSES): a benchmarking platform for molecular generation models. Frontiers Pharmacol. 11, 565644 (2020)","journal-title":"Frontiers Pharmacol."},{"issue":"10","key":"6_CR30","doi-asserted-by":"publisher","first-page":"4398","DOI":"10.1021\/acs.molpharmaceut.8b00839","volume":"15","author":"D Polykovskiy","year":"2018","unstructured":"Polykovskiy, D., et al.: Entangled conditional adversarial autoencoder for de novo drug discovery. Mol. Pharm. 15(10), 4398\u20134405 (2018)","journal-title":"Mol. Pharm."},{"key":"6_CR31","unstructured":"Popova, M., Shvets, M., Oliva, J., Isayev, O.: MolecularRNN: Generating realistic molecular graphs with optimized properties. [cs, q-bio, stat] arXiv:1905.13372 (2019)"},{"key":"6_CR32","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.asoc.2017.01.038","volume":"55","author":"M Ravber","year":"2017","unstructured":"Ravber, M., Mernik, M., \u010crepin\u0161ek, M.: The impact of quality indicators on the rating of multi-objective evolutionary algorithms. Appl. Soft. Comput. 55, 265\u2013275 (2017)","journal-title":"Appl. Soft. Comput."},{"issue":"5","key":"6_CR33","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers, D., Hahn, M.: Extended-connectivity fingerprints. J. Chem. Inf. Model. 50(5), 742\u2013754 (2010)","journal-title":"J. Chem. Inf. Model."},{"key":"6_CR34","doi-asserted-by":"crossref","unstructured":"Samanta, et al.: NeVAE: a deep generative model for molecular graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1110\u20131117 (2019)","DOI":"10.1609\/aaai.v33i01.33011110"},{"issue":"3","key":"6_CR35","doi-asserted-by":"publisher","first-page":"1182","DOI":"10.1021\/acs.jcim.8b00751","volume":"59","author":"B Sattarov","year":"2019","unstructured":"Sattarov, B., Baskin, I.I., Horvath, D., Marcou, G., Bjerrum, E.J., Varnek, A.: De novo molecular design by combining deep deep autoencoder recurrent neural networks with generative topographic mapping. J. Chem. Inf. Model. 59(3), 1182\u20131196 (2019)","journal-title":"J. Chem. Inf. Model."},{"issue":"1","key":"6_CR36","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1021\/acscentsci.7b00512","volume":"4","author":"MH Segler","year":"2018","unstructured":"Segler, M.H., Kogej, T., Tyrchan, C., Waller, M.P.: Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Central Sci. 4(1), 120\u2013131 (2018)","journal-title":"ACS Central Sci."},{"issue":"1","key":"6_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-020-00429-4","volume":"12","author":"JO Spiegel","year":"2020","unstructured":"Spiegel, J.O., Durrant, J.D.: AutoGrow4: an open-source genetic algorithm for de novo drug design and lead optimization. J. Cheminformatics 12(1), 1\u201316 (2020). https:\/\/doi.org\/10.1186\/s13321-020-00429-4","journal-title":"J. Cheminformatics"},{"issue":"34","key":"6_CR38","doi-asserted-by":"publisher","first-page":"8016","DOI":"10.1039\/C9SC01928F","volume":"10","author":"R Winter","year":"2019","unstructured":"Winter, R., Montanari, F., Steffen, A., Briem, H., No\u00e9, F., Clevert, D.A.: Efficient multi-objective molecular optimization in a continuous latent space. Chem. Sci. 10(34), 8016\u20138024 (2019)","journal-title":"Chem. Sci."},{"issue":"11","key":"6_CR39","doi-asserted-by":"publisher","first-page":"1431","DOI":"10.1246\/cl.180665","volume":"47","author":"N Yoshikawa","year":"2018","unstructured":"Yoshikawa, N., Terayama, K., Sumita, M., Homma, T., Oono, K., Tsuda, K.: Population-based de novo molecule generation, using grammatical evolution. Chem. Lett. 47(11), 1431\u20131434 (2018)","journal-title":"Chem. Lett."},{"key":"6_CR40","first-page":"6410","volume":"31","author":"J You","year":"2018","unstructured":"You, J., Liu, B., Ying, Z., Pande, V., Leskovec, J.: Graph convolutional policy network for goal-directed molecular graph generation. Adv. Neural Inf. Process. Syst. 31, 6410\u20136421 (2018)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"6_CR41","first-page":"95","volume":"3242","author":"E Zitzler","year":"2001","unstructured":"Zitzler, E., Laumanns, M., Thiele, L.: Spea 2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. Evol. Methods Des. Optim. and Control Appl. Ind. Probl. 3242, 95\u2013100 (2001)","journal-title":"Evol. Methods Des. Optim. and Control Appl. Ind. Probl."}],"container-title":["Lecture Notes in Computer Science","Applications of Evolutionary Computation"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-72699-7_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T06:49:47Z","timestamp":1724741387000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-72699-7_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030726980","9783030726997"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72699-7_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 April 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EvoApplications","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on the Applications of Evolutionary Computation (Part of EvoStar)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 April 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 April 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"evoapplications2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.evostar.org\/2021\/evoapps\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"78","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"65% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.38","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.04","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the Corona pandemic this event was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}