{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T10:21:32Z","timestamp":1772187692909,"version":"3.50.1"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032045515","type":"print"},{"value":"9783032045522","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-04552-2_3","type":"book-chapter","created":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T14:15:00Z","timestamp":1758550500000},"page":"17-28","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ADMETrix: ADMET-Driven De Novo Molecular Generation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9838-3793","authenticated-orcid":false,"given":"Nikolaos","family":"Mourdoukoutas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2521-2996","authenticated-orcid":false,"given":"Aigli","family":"Korfiati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1593-7491","authenticated-orcid":false,"given":"Vassilis","family":"Pitsikalis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"issue":"8","key":"3_CR1","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., Madzhidov, T.I., Varnek, A.: Estimation of the size of drug-like chemical space based on GDB-17 data. J. Comput. Aided Mol. Des. 27(8), 675\u2013679 (2013). https:\/\/doi.org\/10.1007\/s10822-013-9672-4","journal-title":"J. Comput. Aided Mol. Des."},{"issue":"D1","key":"3_CR2","doi-asserted-by":"publisher","first-page":"D1202","DOI":"10.1093\/nar\/gkv951","volume":"44","author":"S Kim","year":"2016","unstructured":"Kim, S., et al.: PubChem substance and compound databases. Nucl. Acids Res. 44(D1), D1202 (2016)","journal-title":"Nucl. Acids Res."},{"issue":"8","key":"3_CR3","doi-asserted-by":"publisher","first-page":"978","DOI":"10.1016\/j.drudis.2015.03.010","volume":"20","author":"M Munson","year":"2015","unstructured":"Munson, M., et al.: Lead optimization attrition analysis (LOAA): a novel and general methodology for medicinal chemistry. Drug Discov. Today 20(8), 978\u2013987 (2015)","journal-title":"Drug Discov. Today"},{"key":"3_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-017-0235-x","volume":"9","author":"M Olivecrona","year":"2017","unstructured":"Olivecrona, M., et al.: Molecular de-novo design through deep reinforcement learning. J. Cheminformatics 9, 1\u201314 (2017)","journal-title":"J. Cheminformatics"},{"key":"3_CR5","unstructured":"Guimaraes, G.L., et al.: Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models. arXiv preprint arXiv:1705.10843 (2017)"},{"key":"3_CR6","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1023\/A:1022672621406","volume":"8","author":"RJ Williams","year":"1992","unstructured":"Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8, 229\u2013256 (1992)","journal-title":"Mach. Learn."},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation (1985)","DOI":"10.21236\/ADA164453"},{"key":"3_CR8","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"3_CR9","unstructured":"Jin, W., Barzilay, R., Jaakkola, T.: Junction tree variational autoencoder for molecular graph generation. In: International Conference on Machine Learning. PMLR (2018)"},{"issue":"4","key":"3_CR10","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1038\/s42256-023-00639-z","volume":"5","author":"J Born","year":"2023","unstructured":"Born, J., Manica, M.: Regression transformer enables concurrent sequence regression and generation for molecular language modelling. Nat. Mach. Intell. 5(4), 432\u2013444 (2023)","journal-title":"Nat. Mach. Intell."},{"key":"3_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-018-0286-7","volume":"10","author":"J Lim","year":"2018","unstructured":"Lim, J., et al.: Molecular generative model based on conditional variational autoencoder for de novo molecular design. J. Cheminformatics 10, 1\u20139 (2018)","journal-title":"J. Cheminformatics"},{"key":"3_CR12","unstructured":"Guo, J., Philippe, S.: Saturn: sample-efficient generative molecular design using memory manipulation. arXiv preprint arXiv:2405.17066 (2024)"},{"key":"3_CR13","unstructured":"De Cao, N., Thomas, K.: MolGAN: an implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973 (2018)"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Fu, T., et al.: Mimosa: multi-constraint molecule sampling for molecule optimization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 1 (2021)","DOI":"10.1609\/aaai.v35i1.16085"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Vignac, C., et al.: Midi: mixed graph and 3D denoising diffusion for molecule generation. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer Nature Switzerland, Cham (2023)","DOI":"10.1007\/978-3-031-43415-0_33"},{"key":"3_CR16","doi-asserted-by":"publisher","unstructured":"Loeffler, H.H., He, J., Tibo, A., et al.: Reinvent 4: modern AI\u2013driven generative molecule design. J. Cheminform. 16, 20 (2024). https:\/\/doi.org\/10.1186\/s13321-024-00812-5","DOI":"10.1186\/s13321-024-00812-5"},{"key":"3_CR17","doi-asserted-by":"publisher","first-page":"D930","DOI":"10.1093\/nar\/gky1075","volume":"47","author":"D Mendez","year":"2019","unstructured":"Mendez, D., et al.: ChEMBL: towards direct deposition of bioassay data. Nucl. Acids Res 47, D930-40 (2019)","journal-title":"Nucl. Acids Res"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Swanson, K., et al.: ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries. Bioinformatics 40(7) (2024)","DOI":"10.1093\/bioinformatics\/btae416"},{"key":"3_CR19","unstructured":"Huang, K., et al.: Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development. arXiv preprint arXiv:2102.09548 (2021)"},{"key":"3_CR20","unstructured":"Guo, J., Schwaller, P.: Beam enumeration: probabilistic explainability for sample efficient self-conditioned molecular design. arXiv preprint arXiv:2309.13957 (2023)"},{"key":"3_CR21","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."},{"issue":"3","key":"3_CR22","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1021\/acs.jcim.8b00839","volume":"59","author":"N Brown","year":"2019","unstructured":"Brown, N., et al.: GuacaMol: benchmarking models for de novo molecular design. J. Chem. Inf. Model. 59(3), 1096\u20131108 (2019)","journal-title":"J. Chem. Inf. Model."},{"key":"3_CR23","unstructured":"RDKit: Open-source cheminformatics. https:\/\/www.rdkit.org"},{"issue":"9","key":"3_CR24","doi-asserted-by":"publisher","first-page":"1736","DOI":"10.1021\/acs.jcim.8b00234","volume":"58","author":"K Preuer","year":"2018","unstructured":"Preuer, K., et al.: Fr\u00e9chet ChemNet distance: a metric for generative models for molecules in drug discovery. J. Chem. Inf. Model. 58(9), 1736\u20131741 (2018)","journal-title":"J. Chem. Inf. Model."},{"issue":"12","key":"3_CR25","doi-asserted-by":"publisher","first-page":"3567","DOI":"10.1039\/C8SC05372C","volume":"10","author":"JH Jensen","year":"2019","unstructured":"Jensen, J.H.: A graph-based genetic algorithm and generative model\/Monte Carlo tree search for the exploration of chemical space. Chem. Sci. 10(12), 3567\u20133572 (2019)","journal-title":"Chem. Sci."},{"issue":"2","key":"3_CR26","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 Cent. Sci. 4(2), 268\u2013276 (2018)","journal-title":"ACS Cent. Sci."},{"issue":"10","key":"3_CR27","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."},{"issue":"19","key":"3_CR28","doi-asserted-by":"publisher","first-page":"2894","DOI":"10.1002\/(SICI)1521-3773(19991004)38:19<2894::AID-ANIE2894>3.0.CO;2-F","volume":"38","author":"G Schneider","year":"1999","unstructured":"Schneider, G., Neidhart, W., Giller, T., Schmid, G.: \u201cScaffold-Hopping\u2019\u2019 by topological pharmacophore search: a contribution to virtual screening. Angew. Chem. Int. Ed. Engl. 38(19), 2894\u20132896 (1999)","journal-title":"Angew. Chem. Int. Ed. Engl."},{"issue":"4","key":"3_CR29","doi-asserted-by":"publisher","first-page":"1238","DOI":"10.1021\/acs.jmedchem.6b01437","volume":"60","author":"Y Hu","year":"2017","unstructured":"Hu, Y., Stumpfe, D., Bajorath, J.: Recent advances in scaffold hopping. J. Med. Chem. 60(4), 1238\u20131246 (2017)","journal-title":"J. Med. Chem."},{"issue":"1","key":"3_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-021-00565-5","volume":"13","author":"S Zheng","year":"2021","unstructured":"Zheng, S., Lei, Z., Ai, H., Chen, H., Deng, D., Yang, Y.: Deep scaffold hopping with multimodal transformer neural networks. J. Cheminformatics 13(1), 1\u201315 (2021). https:\/\/doi.org\/10.1186\/s13321-021-00565-5","journal-title":"J. Cheminformatics"},{"issue":"1","key":"3_CR31","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1186\/s13321-023-00766-0","volume":"15","author":"C Hu","year":"2023","unstructured":"Hu, C., et al.: ScaffoldGVAE: scaffold generation and hopping of drug molecules via a variational autoencoder based on multi-view graph neural networks. J. Cheminformatics 15(1), 91 (2023)","journal-title":"J. Cheminformatics"},{"key":"3_CR32","doi-asserted-by":"crossref","unstructured":"Rossen, L., et al.: Scaffold Hopping with Generative Reinforcement Learning (2024)","DOI":"10.26434\/chemrxiv-2024-gd3j4"},{"issue":"3","key":"3_CR33","first-page":"108","volume":"13","author":"T Delungahawatta","year":"2023","unstructured":"Delungahawatta, T., Pokharel, A., Paz, R., Haas, C.J.: Topical diclofenac-induced hepatotoxicity. J. Community Hosp. Intern. Med. Perspect. 13(3), 108\u2013112 (2023)","journal-title":"J. Community Hosp. Intern. Med. Perspect."},{"issue":"2","key":"3_CR34","first-page":"599","volume":"77","author":"Z Abed Al-Kareem","year":"2022","unstructured":"Abed Al-Kareem, Z., Aziz, N.D., Ali Zghair, M.: Hepatoprotective effect of coenzyme Q10 in rats with diclofenac toxicity. Arch. Razi Inst. 77(2), 599\u2013605 (2022)","journal-title":"Arch. Razi Inst."},{"issue":"46","key":"3_CR35","doi-asserted-by":"publisher","first-page":"45763","DOI":"10.1074\/jbc.M305481200","volume":"278","author":"SW Rowlinson","year":"2003","unstructured":"Rowlinson, S.W., et al.: A novel mechanism of cyclooxygenase-2 inhibition involving interactions with Ser-530 and Tyr-385. J. Biol. Chem. 278(46), 45763\u201345769 (2003)","journal-title":"J. Biol. Chem."},{"key":"3_CR36","doi-asserted-by":"crossref","unstructured":"Eberhardt, J., et al.: AutoDock Vina 1.2.0: new docking methods, expanded force field, and python bindings. J. Chem. Inf. Model. 61(8), 3891\u20133898 (2021)","DOI":"10.1021\/acs.jcim.1c00203"}],"updated-by":[{"DOI":"10.1007\/978-3-032-04552-2_21","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:00:00Z","timestamp":1772236800000}}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning. ICANN 2025 International Workshops and Special Sessions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04552-2_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T09:22:39Z","timestamp":1772184159000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04552-2_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,23]]},"ISBN":["9783032045515","9783032045522"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04552-2_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,23]]},"assertion":[{"value":"23 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"28 February 2026","order":2,"name":"change_date","label":"Change Date","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Correction","order":3,"name":"change_type","label":"Change Type","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"A correction has been published.","order":4,"name":"change_details","label":"Change Details","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kaunas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"34","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}