{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:33:08Z","timestamp":1759336388226,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811978661"},{"type":"electronic","value":"9789811978678"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-19-7867-8_2","type":"book-chapter","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T12:02:18Z","timestamp":1683288138000},"page":"13-22","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Direct De Novo Molecule Generation Using Probabilistic Diverse Variational Autoencoder"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1708-367X","authenticated-orcid":false,"given":"Arun","family":"Singh Bhadwal","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8063-8650","authenticated-orcid":false,"given":"Kamal","family":"Kumar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,6]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Xu, Y., et\u00a0al.: Deep learning for molecular generation. Future Med. Chem. 11(6), 567\u2013597 (2019)","DOI":"10.4155\/fmc-2018-0358"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Elton, D.C., et\u00a0al.: Deep learning for molecular design-a review of the state of the art. Mol. Syst. Des. Eng. 4(4), 828\u2013849 (2019)","DOI":"10.1039\/C9ME00039A"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Vamathevan, J., et\u00a0al.: Applications of machine learning in drug discovery and development. Nature Rev. Drug Discovery 18(6), 463\u2013477 (2019)","DOI":"10.1038\/s41573-019-0024-5"},{"issue":"6400","key":"2_CR4","doi-asserted-by":"publisher","first-page":"360","DOI":"10.1126\/science.aat2663","volume":"361","author":"Benjamin Sanchez-Lengeling","year":"2018","unstructured":"Sanchez-Lengeling, Benjamin, Aspuru-Guzik, Al\u00e1in.: Inverse molecular design using machine learning: generative models for matter engineering. Science 361(6400), 360\u2013365 (2018)","journal-title":"Science"},{"key":"2_CR5","unstructured":"Lopyrev, K.: Generating news headlines with recurrent neural networks. arXiv preprint arXiv:1512.01712 (2015)"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Briot, J.-P., Hadjeres, G., Pachet, F.-D.: Deep learning techniques for music generation. Springer (2020)","DOI":"10.1007\/978-3-319-70163-9"},{"key":"2_CR7","unstructured":"Wang, Z., He, W., Wu, H., Wu, H., Li, W., Wang, H., Chen, E.E.: Chinese poetry generation with planning based neural network. arXiv preprint arXiv:1610.09889 (2016)"},{"key":"2_CR8","unstructured":"Elgammal, A., et\u00a0al.: Can: creative adversarial networks, generating art by learning about styles and deviating from style norms. arXiv preprint arXiv:1706.07068 (2017)"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Segler, M.H., Preuss, M., Waller, M.P.: Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555(7698), 604\u2013610 (2018)","DOI":"10.1038\/nature25978"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234\u2013241. Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Chen, H., et\u00a0al.: The rise of deep learning in drug discovery. Drug Discovery Today 23(6), 1241\u20131250 (2018)","DOI":"10.1016\/j.drudis.2018.01.039"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Weininger, D: SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28(1), 31\u201336 (1988)","DOI":"10.1021\/ci00057a005"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Schwalbe-Koda, D., G\u00f3mez-Bombarelli, R.: Generative models for automatic chemical design. In: Machine Learning Meets Quantum Physics, pp. 445\u2013467. Springer, Cham (2020)","DOI":"10.1007\/978-3-030-40245-7_21"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Ar\u00fas-Pous, J., et\u00a0al.: Exploring the GDB-13 chemical space using deep generative models. J. Cheminformatics 11(1), 1\u201314 (2019)","DOI":"10.1186\/s13321-019-0341-z"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Polykovskiy, D., et\u00a0al.: Molecular sets (MOSES): a benchmarking platform for molecular generation models. Front. Pharmacol 11, 1931 (2020)","DOI":"10.3389\/fphar.2020.565644"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Bombarelli, R., et\u00a0al.: Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Sci. 4(2), 268-276 (2018)","DOI":"10.1021\/acscentsci.7b00572"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Winter, R., et\u00a0al.: Efficient multi-objective molecular optimization in a continuous latent space. Chem. Sci. 10(34), 8016\u20138024 (2019)","DOI":"10.1039\/C9SC01928F"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Jannik Bjerrum, E., Sattarov, B.: Improving chemical autoencoder latent space and molecular De novo generation diversity with heteroencoders. arXiv e-prints: arXiv-1806 (2018)","DOI":"10.3390\/biom8040131"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Lim, J., et\u00a0al.: Molecular generative model based on conditional variational autoencoder for de novo molecular design. J. Cheminformatics 10(1), 1\u20139 (2018)","DOI":"10.1186\/s13321-018-0286-7"},{"key":"2_CR20","unstructured":"Landrum, G.: RDKit: Open-source cheminformatics. (Online). http:\/\/wwwrdkit.org. Accessed 3 Jan 2022, 2012 (2006)"},{"key":"2_CR21","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"2_CR22","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Adv. Neural Inf. Process. Syst. 27 (2014)"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Preuer, K., et\u00a0al.: Fr\u00e9chet ChemNet distance: a metric for generative models for molecules in drug discovery. J. Chem. Inf. Model. 58(9), 1736\u20131741 (2018)","DOI":"10.1021\/acs.jcim.8b00234"}],"container-title":["Lecture Notes in Networks and Systems","Computer Vision and Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-7867-8_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T12:03:45Z","timestamp":1683288225000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-7867-8_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789811978661","9789811978678"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-7867-8_2","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"6 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}