{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T17:49:08Z","timestamp":1781632148213,"version":"3.54.5"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"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":["Nat Mach Intell"],"DOI":"10.1038\/s42256-021-00403-1","type":"journal-article","created":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T00:13:50Z","timestamp":1634602430000},"page":"914-922","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":176,"title":["Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning"],"prefix":"10.1038","volume":"3","author":[{"given":"Jike","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang-Yu","family":"Hsieh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaorui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenxing","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dejun","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benben","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xujun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiaojun","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongsheng","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7227-2580","authenticated-orcid":false,"given":"Tingjun","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,10,18]]},"reference":[{"key":"403_CR1","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1039\/C9ME00039A","volume":"4","author":"DC Elton","year":"2019","unstructured":"Elton, D. C., Boukouvalas, Z., Fuge, M. D. & Chung, P. W. Deep learning for molecular design-a review of the state of the art. Mol. Syst. Design Eng. 4, 828\u2013849 (2019).","journal-title":"Mol. Syst. Design Eng."},{"key":"403_CR2","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1016\/j.drudis.2018.01.039","volume":"23","author":"H Chen","year":"2018","unstructured":"Chen, H., Engkvist, O., Wang, Y., Olivecrona, M. & Blaschke, T. The rise of deep learning in drug discovery. Drug Discov. Today 23, 1241\u20131250 (2018).","journal-title":"Drug Discov. Today"},{"key":"403_CR3","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1016\/j.tips.2019.09.004","volume":"40","author":"H Chen","year":"2019","unstructured":"Chen, H. & Engkvist, O. Has drug design augmented by artificial intelligence become a reality? Trends Pharmacol. Sci. 40, 806\u2013809 (2019).","journal-title":"Trends Pharmacol. Sci."},{"key":"403_CR4","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1038\/s41563-019-0338-z","volume":"18","author":"S Ekins","year":"2019","unstructured":"Ekins, S. et al. Exploiting machine learning for end-to-end drug discovery and development. Nat. Mater. 18, 435\u2013441 (2019).","journal-title":"Nat. Mater."},{"key":"403_CR5","doi-asserted-by":"publisher","first-page":"2545","DOI":"10.1021\/acs.jcim.9b00266","volume":"59","author":"AC Mater","year":"2019","unstructured":"Mater, A. C. & Coote, M. L. Deep learning in chemistry. J. Chem. Inf. Model. 59, 2545\u20132559 (2019).","journal-title":"J. Chem. Inf. Model."},{"key":"403_CR6","doi-asserted-by":"publisher","first-page":"1700133","DOI":"10.1002\/minf.201700133","volume":"37","author":"PB J\u00f8rgensen","year":"2018","unstructured":"J\u00f8rgensen, P. B., Schmidt, M. N. & Winther, O. Deep generative models for molecular science. Mol. Inf. 37, 1700133 (2018).","journal-title":"Mol. Inf."},{"key":"403_CR7","doi-asserted-by":"publisher","first-page":"10520","DOI":"10.1021\/acs.chemrev.8b00728","volume":"119","author":"X Yang","year":"2019","unstructured":"Yang, X., Wang, Y., Byrne, R., Schneider, G. & Yang, S. Concepts of artificial intelligence for computer-assisted drug discovery. Chem. Rev. 119, 10520\u201310594 (2019).","journal-title":"Chem. Rev."},{"key":"403_CR8","doi-asserted-by":"publisher","first-page":"2520","DOI":"10.3390\/molecules23102520","volume":"23","author":"G Hessler","year":"2018","unstructured":"Hessler, G. & Baringhaus, K.-H. Artificial intelligence in drug design. Molecules 23, 2520 (2018).","journal-title":"Molecules"},{"key":"403_CR9","doi-asserted-by":"publisher","first-page":"2783","DOI":"10.3390\/ijms20112783","volume":"20","author":"M Batool","year":"2019","unstructured":"Batool, M., Ahmad, B. & Choi, S. A structure-based drug discovery paradigm. Int. J. Mol. Sci. 20, 2783 (2019).","journal-title":"Int. J. Mol. Sci."},{"key":"403_CR10","doi-asserted-by":"publisher","first-page":"567","DOI":"10.4155\/fmc-2018-0358","volume":"11","author":"Y Xu","year":"2019","unstructured":"Xu, Y. et al. Deep learning for molecular generation. Future Med. Chem. 11, 567\u2013597 (2019).","journal-title":"Future Med. Chem."},{"key":"403_CR11","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1038\/s42256-019-0067-7","volume":"1","author":"A Button","year":"2019","unstructured":"Button, A., Merk, D., Hiss, J. A. & Schneider, G. Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis. Nat. Mach. Intell. 1, 307\u2013315 (2019).","journal-title":"Nat. Mach. Intell."},{"key":"403_CR12","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1038\/s42256-020-0160-y","volume":"2","author":"M Moret","year":"2020","unstructured":"Moret, M., Friedrich, L., Grisoni, F., Merk, D. & Schneider, G. Generative molecular design in low data regimes. Nat. Mach. Intell. 2, 171\u2013180 (2020).","journal-title":"Nat. Mach. Intell."},{"key":"403_CR13","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, 268\u2013276 (2018).","journal-title":"ACS Central Sci."},{"key":"403_CR14","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1038\/s41587-019-0224-x","volume":"37","author":"A Zhavoronkov","year":"2019","unstructured":"Zhavoronkov, A. et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol. 37, 1038\u20131040 (2019).","journal-title":"Nat. Biotechnol."},{"key":"403_CR15","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. Pharmaceutics 15, 4398\u20134405 (2018).","journal-title":"Mol. Pharmaceutics"},{"key":"403_CR16","doi-asserted-by":"publisher","first-page":"4386","DOI":"10.1021\/acs.molpharmaceut.7b01137","volume":"15","author":"E Putin","year":"2018","unstructured":"Putin, E. et al. Adversarial threshold neural computer for molecular de novo design. Mol. Pharm. 15, 4386\u20134397 (2018).","journal-title":"Mol. Pharm."},{"key":"403_CR17","unstructured":"Bjerrum, E. J. & Threlfall, R. Molecular generation with recurrent neural networks (RNNs). Preprint at https:\/\/arxiv.org\/abs\/1705.04612 (2017)."},{"key":"403_CR18","doi-asserted-by":"publisher","first-page":"1700111","DOI":"10.1002\/minf.201700111","volume":"37","author":"A Gupta","year":"2018","unstructured":"Gupta, A. et al. Generative recurrent networks for de novo drug design. Mol. Inf. 37, 1700111 (2018).","journal-title":"Mol. Inf."},{"key":"403_CR19","doi-asserted-by":"publisher","first-page":"1136","DOI":"10.1021\/acs.jcim.8b00626","volume":"59","author":"P Pog\u00e1ny","year":"2019","unstructured":"Pog\u00e1ny, P., Arad, N., Genway, S. & Pickett, S. D. De novo molecule design by translating from reduced graphs to SMILES. J. Chem. Inf. Model. 59, 1136\u20131146 (2019).","journal-title":"J. Chem. Inf. Model."},{"key":"403_CR20","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s13321-019-0355-6","volume":"11","author":"X Liu","year":"2019","unstructured":"Liu, X., Ye, K., van Vlijmen, H. W. T., Ijzerman, A. P. & van Westen, G. J. P. An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A2A receptor. J. Cheminf. 11, 35 (2019).","journal-title":"J. Cheminf."},{"key":"403_CR21","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1021\/acscentsci.7b00512","volume":"4","author":"MHS Segler","year":"2018","unstructured":"Segler, M. H. S., Kogej, T., Tyrchan, C. & Waller, M. P. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Central Sci. 4, 120\u2013131 (2018).","journal-title":"ACS Central Sci."},{"key":"403_CR22","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1080\/14686996.2017.1401424","volume":"18","author":"X Yang","year":"2017","unstructured":"Yang, X., Zhang, J., Yoshizoe, K., Terayama, K. & Tsuda, K. ChemTS: an efficient python library for de novo molecular generation. Sci. Technol. Adv. Mater. 18, 972\u2013976 (2017).","journal-title":"Sci. Technol. Adv. Mater."},{"key":"403_CR23","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1021\/acs.jcim.9b00943","volume":"60","author":"F Grisoni","year":"2020","unstructured":"Grisoni, F., Moret, M., Lingwood, R. & Schneider, G. Bidirectional molecule generation with recurrent neural networks. J. Chem. Inf. Model. 60, 1175\u20131183 (2020).","journal-title":"J. Chem. Inf. Model."},{"key":"403_CR24","doi-asserted-by":"publisher","first-page":"1700153","DOI":"10.1002\/minf.201700153","volume":"37","author":"D Merk","year":"2018","unstructured":"Merk, D., Friedrich, L., Grisoni, F. & Schneider, G. De novo design of bioactive small molecules by artificial intelligence. Mol. Inf. 37, 1700153 (2018).","journal-title":"Mol. Inf."},{"key":"403_CR25","doi-asserted-by":"publisher","first-page":"eaap7885","DOI":"10.1126\/sciadv.aap7885","volume":"4","author":"M Popova","year":"2018","unstructured":"Popova, M., Isayev, O. & Tropsha, A. Deep reinforcement learning for de novo drug design. Sci. Adv. 4, eaap7885 (2018).","journal-title":"Sci. Adv."},{"key":"403_CR26","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s13321-017-0235-x","volume":"9","author":"M Olivecrona","year":"2017","unstructured":"Olivecrona, M., Blaschke, T., Engkvist, O. & Chen, H. Molecular de-novo design through deep reinforcement learning. J. Cheminf. 9, 48 (2017).","journal-title":"J. Cheminf."},{"key":"403_CR27","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1186\/s13321-018-0286-7","volume":"10","author":"J Lim","year":"2018","unstructured":"Lim, J., Ryu, S., Kim, J. W. & Kim, W. Y. Molecular generative model based on conditional variational autoencoder for de novo molecular design. J. Cheminf. 10, 31 (2018).","journal-title":"J. Cheminf."},{"key":"403_CR28","unstructured":"Kusner, M. J., Paige, B. & Hern\u00e1ndez-Lobato, J. M. in Proc. 34th International Conference on Machine Learning Vol. 70. (eds. Doina, P. & Yee Whye, T.) 1945\u20131954 (PMLR, 2017)."},{"key":"403_CR29","unstructured":"Liu, Q., Allamanis, M., Brockschmidt, M. & Gaunt, A. L. in Proc. 32nd International Conference on Neural Information Processing Systems 7806\u20137815 (Curran Associates Inc., 2018)."},{"key":"403_CR30","doi-asserted-by":"crossref","unstructured":"Simonovsky, M. & Komodakis, N. in International Conference on Artificial Neural Networks 412\u2013422 (Springer, 2018).","DOI":"10.1007\/978-3-030-01418-6_41"},{"key":"403_CR31","doi-asserted-by":"publisher","first-page":"131","DOI":"10.3390\/biom8040131","volume":"8","author":"EJ Bjerrum","year":"2018","unstructured":"Bjerrum, E. J. & Sattarov, B. Improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders. Biomolecules 8, 131 (2018).","journal-title":"Biomolecules"},{"key":"403_CR32","unstructured":"Jin, W., Barzilay, R. & Jaakkola, T. in Proc. 35th International Conference on Machine Learning Vol. 80. (eds. Jennifer, D. & Andreas, K.) 2323\u20132332 (PMLR, 2018)."},{"key":"403_CR33","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1021\/acs.jcim.8b00263","volume":"59","author":"S Kang","year":"2019","unstructured":"Kang, S. & Cho, K. Conditional molecular design with deep generative models. J. Chem. Inf. Model. 59, 43\u201352 (2019).","journal-title":"J. Chem. Inf. Model."},{"key":"403_CR34","unstructured":"Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. Preprint at https:\/\/arxiv.org\/abs\/1312.6114 (2014)."},{"key":"403_CR35","doi-asserted-by":"publisher","first-page":"3098","DOI":"10.1021\/acs.molpharmaceut.7b00346","volume":"14","author":"A Kadurin","year":"2017","unstructured":"Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A. & Zhavoronkov, A. druGAN: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico. Mol. Pharmaceutics 14, 3098\u20133104 (2017).","journal-title":"Mol. Pharmaceutics"},{"key":"403_CR36","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). Preprint at ChemRxiv https:\/\/doi.org\/10.26434\/chemrxiv.5309668.v3 (2017).","DOI":"10.26434\/chemrxiv.5309668.v3"},{"key":"403_CR37","unstructured":"Guimaraes, G. L., Sanchez-Lengeling, B., Farias, P. L. C. & Aspuru-Guzik, A. Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models. Preprint at https:\/\/arxiv.org\/abs\/1705.10843 (2017)."},{"key":"403_CR38","doi-asserted-by":"publisher","first-page":"1194","DOI":"10.1021\/acs.jcim.7b00690","volume":"58","author":"E Putin","year":"2018","unstructured":"Putin, E. et al. Reinforced adversarial neural computer for de novo molecular design. J. Chem. Inf. Model. 58, 1194\u20131204 (2018).","journal-title":"J. Chem. Inf. Model."},{"key":"403_CR39","doi-asserted-by":"crossref","unstructured":"Yu, L., Zhang, W., Wang, J. & Yu, Y. in Proc. 31st AAAI Conference on Artificial Intelligence 2852\u20132858 (AAAI Press, 2017).","DOI":"10.1609\/aaai.v31i1.10804"},{"key":"403_CR40","unstructured":"Sohn, K., Yan, X. & Lee, H. in Proc. 28th International Conference on Neural Information Processing Systems Vol. 2, 3483\u20133491 (MIT Press, 2015)."},{"key":"403_CR41","unstructured":"You, J., Liu, B., Ying, Z., Pande, V. & Leskovec, J. in Advances in Neural Information Processing Systems 6410\u20136421 (2018)."},{"key":"403_CR42","unstructured":"Brochu, E., Cora, V. M. & Freitas, N. d. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. Preprint at https:\/\/arxiv.org\/abs\/\/1012.2599 (2010)."},{"key":"403_CR43","unstructured":"Cao, N. D. & Kipf, T. MolGAN: an implicit generative model for small molecular graphs. Preprint at https:\/\/arxiv.org\/abs\/1805.11973 (2018)."},{"key":"403_CR44","unstructured":"Jaques, N. et al. in Proc. 34th International Conference on Machine Learning Vol. 70, 1645\u20131654 (JMLR.org, 2017)."},{"key":"403_CR45","unstructured":"Sutton, R. S. & Barto, A. G. Introduction to Reinforcement Learning (MIT Press, 1998)."},{"key":"403_CR46","doi-asserted-by":"publisher","first-page":"5918","DOI":"10.1021\/acs.jcim.0c00915","volume":"60","author":"T Blaschke","year":"2020","unstructured":"Blaschke, T. et al. REINVENT 2.0: an AI tool for de novo drug design. J. Chem. Inf. Model. 60, 5918\u20135922 (2020).","journal-title":"J. Chem. Inf. Model."},{"key":"403_CR47","first-page":"5998","volume":"30","author":"A Vaswani","year":"2017","unstructured":"Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Proc. Syst. 30, 5998\u20136008 (2017).","journal-title":"Adv. Neural Inf. Proc. Syst."},{"key":"403_CR48","unstructured":"Tripp, A., Daxberger, E. & Hern\u00e1ndez-Lobato, J. M. in Advances in Neural Information Processing Systems 11259\u201311272 (2020)."},{"key":"403_CR49","doi-asserted-by":"publisher","first-page":"2887","DOI":"10.1021\/jm9602928","volume":"39","author":"GW Bemis","year":"1996","unstructured":"Bemis, G. W. & Murcko, M. A. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887\u20132893 (1996).","journal-title":"J. Med. Chem."},{"key":"403_CR50","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1186\/s13321-020-00473-0","volume":"12","author":"T Blaschke","year":"2020","unstructured":"Blaschke, T., Engkvist, O., Bajorath, J. & Chen, H. Memory-assisted reinforcement learning for diverse molecular de novo design. J. Cheminf. 12, 68 (2020).","journal-title":"J. Cheminf."},{"key":"403_CR51","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1093\/nar\/gkr777","volume":"40","author":"G Anna","year":"2012","unstructured":"Anna, G. et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, 1100\u20131107 (2012).","journal-title":"Nucleic Acids Res."},{"key":"403_CR52","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/S0955-0674(98)80143-9","volume":"10","author":"YT Ip","year":"1998","unstructured":"Ip, Y. T. & Davis, R. J. Signal transduction by the c-Jun N-terminal kinase (JNK)-from inflammation to development. Curr. Opin. Cell Biol. 10, 205\u2013219 (1998).","journal-title":"Curr. Opin. Cell Biol."},{"key":"403_CR53","doi-asserted-by":"publisher","first-page":"e10092","DOI":"10.1371\/journal.pone.0010092","volume":"5","author":"L Shang","year":"2010","unstructured":"Shang, L. et al. RAGE modulates hypoxia\/reoxygenation injury in adult murine cardiomyocytes via JNK and GSK-3 beta signaling pathways. PLoS ONE 5, e10092 (2010).","journal-title":"PLoS ONE"},{"key":"403_CR54","doi-asserted-by":"publisher","first-page":"e18146","DOI":"10.1371\/journal.pone.0018146","volume":"6","author":"K Tanabe","year":"2011","unstructured":"Tanabe, K. et al. Glucose and fatty acids synergize to promote B-cell apoptosis through activation of glycogen synthase kinase 3 beta independent of JNK activation. PLoS ONE 6, e18146 (2011).","journal-title":"PLoS ONE"},{"key":"403_CR55","first-page":"38","volume":"14","author":"G Hinton","year":"2015","unstructured":"Hinton, G., Vinyals, O. & Dean, J. Distilling the knowledge in a neural network. Computer Sci. 14, 38\u201339 (2015).","journal-title":"Computer Sci."},{"key":"403_CR56","doi-asserted-by":"crossref","unstructured":"Cho, K. et al. Learning phrase representations using RNN Encoder decoder for statistical machine translation. Preprint at https:\/\/arxiv.org\/abs\/1406.1078 (2014).","DOI":"10.3115\/v1\/D14-1179"},{"key":"403_CR57","unstructured":"Jaques, N., Gu, S., Turner, R. E. & Eck, D. Tuning recurrent neural networks with reinforcement learning. Preprint at https:\/\/arxiv.org\/abs\/1611.02796v1 (2017)."},{"key":"403_CR58","unstructured":"Jin, W., Barzilay, R. & Jaakkola, T. Composing molecules with multiple property constraints. Preprint at https:\/\/arxiv.org\/abs\/2002.03244v1 (2020)."},{"key":"403_CR59","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. Random forests. Mach. Learn. 45, 5\u201332 (2001).","journal-title":"Mach. Learn."},{"key":"403_CR60","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"R David","year":"2010","unstructured":"David, R. & Mathew, H. Extended-connectivity fingerprints. J. Chem. Inf. Model. 50, 742\u2013754 (2010).","journal-title":"J. Chem. Inf. Model."},{"key":"403_CR61","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F. et al. Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011).","journal-title":"J. Mach. Learn. Res."},{"key":"403_CR62","doi-asserted-by":"publisher","first-page":"6595","DOI":"10.1021\/acs.chemrev.8b00759","volume":"119","author":"JG Freeze","year":"2019","unstructured":"Freeze, J. G., Kelly, H. R. & Batista, V. S. Search for catalysts by inverse design: artificial intelligence, mountain climbers, and alchemists. Chem. Rev. 119, 6595\u20136612 (2019).","journal-title":"Chem. Rev."},{"key":"403_CR63","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. Front. Pharmacol. 11, 565644 (2020).","journal-title":"Front. Pharmacol."},{"key":"403_CR64","doi-asserted-by":"publisher","unstructured":"Wang J. et al. Code Repository jkwang93\/MCMG: v1.1.0 (Zenodo, 2021); https:\/\/doi.org\/10.5281\/zenodo.5205570","DOI":"10.5281\/zenodo.5205570"}],"container-title":["Nature Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42256-021-00403-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-021-00403-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-021-00403-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,3]],"date-time":"2022-12-03T20:39:05Z","timestamp":1670099945000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42256-021-00403-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,18]]},"references-count":64,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["403"],"URL":"https:\/\/doi.org\/10.1038\/s42256-021-00403-1","relation":{},"ISSN":["2522-5839"],"issn-type":[{"value":"2522-5839","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,18]]},"assertion":[{"value":"9 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}