{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T05:50:01Z","timestamp":1763013001376,"version":"3.45.0"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032079039","type":"print"},{"value":"9783032079046","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:00:00Z","timestamp":1763078400000},"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\/978-3-032-07904-6_21","type":"book-chapter","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T05:48:05Z","timestamp":1763012885000},"page":"234-245","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MMM: Quantum-Chemical Molecular Representation Learning for\u00a0Combinatorial Drug Recommendation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0561-438X","authenticated-orcid":false,"given":"Chongmyung","family":"Kwon","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3028-3165","authenticated-orcid":false,"given":"Yujin","family":"Kim","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0713-2746","authenticated-orcid":false,"given":"Seoeun","family":"Park","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-9544-576X","authenticated-orcid":false,"given":"Yunji","family":"Lee","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8176-252X","authenticated-orcid":false,"given":"Charmgil","family":"Hong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"21_CR1","unstructured":"Choi, E., Bahadori, M.T., Sun, J., Kulas, J., Schuetz, A., Stewart, W.: Retain: an interpretable predictive model for healthcare using reverse time attention mechanism. Adv. Neural Inf. Process. Syst. 29 (2016)"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Pham, T., Tran, T., Phung, D., Venkatesh, S.: Deepcare: a deep dynamic memory model for predictive medicine. In: Advances in Knowledge Discovery and Data Mining: 20th Pacific-Asia Conference, PAKDD 2016, Auckland, New Zealand, April 19\u201322, 2016, Proceedings, Part II 20, pp. 30\u201341. Springer (2016)","DOI":"10.1007\/978-3-319-31750-2_3"},{"issue":"2","key":"21_CR3","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","volume":"14","author":"JL Elman","year":"1990","unstructured":"Elman, J.L.: Finding structure in time. Cogn. Sci. 14(2), 179\u2013211 (1990)","journal-title":"Cogn. Sci."},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Yang, C., Xiao, C., Ma, F., Glass, L., Sun, J.: Safedrug: dual molecular graph encoders for recommending effective and safe drug combinations. arXiv preprint arXiv:2105.02711 (2021)","DOI":"10.24963\/ijcai.2021\/514"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Yang, N., Zeng, K., Qitian, W., Yan, J.: Molerec: combinatorial drug recommendation with substructure-aware molecular representation learning. In: Proceedings of the ACM Web Conference, pp. 4075\u20134085 (2023)","DOI":"10.1145\/3543507.3583872"},{"issue":"1","key":"21_CR6","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61\u201380 (2008)","journal-title":"IEEE Trans. Neural Netw."},{"key":"21_CR7","unstructured":"Guo, Z., et al.: Graph-based molecular representation learning. arXiv preprint arXiv:2207.04869 (2022)"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Zhu, J., et al.: Unified 2D and 3D pre-training of molecular representations. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 2626\u20132636 (2022)","DOI":"10.1145\/3534678.3539368"},{"key":"21_CR9","unstructured":"St\u00e4rk, H., et al.: 3D infomax improves GNNs for molecular property prediction. In: International Conference on Machine Learning, pp. 20479\u201320502. PMLR (2022)"},{"key":"21_CR10","unstructured":"Liu, S., Wang, H., Liu, W., Lasenby, J., Guo, H., Tang, J.: Pre-training molecular graph representation with 3D geometry. arXiv preprint arXiv:2110.07728 (2021)"},{"issue":"3","key":"21_CR11","doi-asserted-by":"publisher","first-page":"85","DOI":"10.18773\/austprescr.2012.037","volume":"35","author":"BD Snyder","year":"2012","unstructured":"Snyder, B.D., Polasek, T.M., Doogue, M.P.: Drug interactions: principles and practice. Aust. Prescriber 35(3), 85\u201388 (2012)","journal-title":"Aust. Prescriber"},{"issue":"7","key":"21_CR12","first-page":"601","volume":"18","author":"C Palleria","year":"2013","unstructured":"Palleria, C., et al.: Pharmacokinetic drug-drug interaction and their implication in clinical management. J. Res. Med. Sci. 18(7), 601 (2013)","journal-title":"J. Res. Med. Sci."},{"issue":"3B","key":"21_CR13","doi-asserted-by":"publisher","first-page":"B864","DOI":"10.1103\/PhysRev.136.B864","volume":"136","author":"P Hohenberg","year":"1964","unstructured":"Hohenberg, P., Kohn, W.: Inhomogeneous electron gas. Phys. Rev. 136(3B), B864 (1964)","journal-title":"Phys. Rev."},{"issue":"3","key":"21_CR14","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1007\/s00214-002-0363-9","volume":"108","author":"P Politzer","year":"2002","unstructured":"Politzer, P., Murray, J.S.: The fundamental nature and role of the electrostatic potential in atoms and molecules. Theor. Chem. Acc. 108(3), 134\u2013142 (2002)","journal-title":"Theor. Chem. Acc."},{"issue":"1","key":"21_CR15","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1002\/prot.21938","volume":"72","author":"L Bendov\u00e1-Biedermannov\u00e1","year":"2008","unstructured":"Bendov\u00e1-Biedermannov\u00e1, L., Hobza, P., Vondr\u00e1\u0161ek, J.: Identifying stabilizing key residues in proteins using interresidue interaction energy matrix. Proteins Struct. Funct. Bioinf. 72(1), 402\u2013413 (2008)","journal-title":"Proteins Struct. Funct. Bioinf."},{"issue":"17","key":"21_CR16","doi-asserted-by":"publisher","first-page":"1808","DOI":"10.1002\/anie.199718081","volume":"36","author":"A Savin","year":"1997","unstructured":"Savin, A., Nesper, R., Wengert, S., F\u00e4ssler, T.F.: Elf: the electron localization function. Angew. Chem. Int. Edit. Eng. 36(17), 1808\u20131832 (1997)","journal-title":"Angew. Chem. Int. Edit. Eng."},{"issue":"9","key":"21_CR17","doi-asserted-by":"publisher","first-page":"5397","DOI":"10.1063\/1.458517","volume":"92","author":"AD Becke","year":"1990","unstructured":"Becke, A.D., Edgecombe, K.E.: A simple measure of electron localization in atomic and molecular systems. J. Chem. Phys. 92(9), 5397\u20135403 (1990)","journal-title":"J. Chem. Phys."},{"key":"21_CR18","unstructured":"Dreizler, R.M., Gross, E.K.U.: Density Functional Theory: An Approach to the Quantum Many-Body Problem. Springer Science & Business Media (2012)"},{"issue":"11","key":"21_CR19","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"2002","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (2002)","journal-title":"Proc. IEEE"},{"issue":"1","key":"21_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AEW Johnson","year":"2016","unstructured":"Johnson, A.E.W., et al.: MIMIC-III, a freely accessible critical care database. Sci. Data 3(1), 1\u20139 (2016)","journal-title":"Sci. Data"},{"issue":"1","key":"21_CR21","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1021\/ci00057a005","volume":"28","author":"D Weininger","year":"1988","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)","journal-title":"J. Chem. Inf. Comput. Sci."},{"issue":"1","key":"21_CR22","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/1758-2946-4-17","volume":"4","author":"MD Hanwell","year":"2012","unstructured":"Hanwell, M.D., Curtis, D.E., Lonie, D.C., Vandermeersch, T., Zurek, E., Hutchison, G.R.: Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J. Cheminf. 4(1), 17 (2012)","journal-title":"J. Cheminf."},{"issue":"2","key":"21_CR23","doi-asserted-by":"publisher","first-page":"e70019","DOI":"10.1002\/wcms.70019","volume":"15","author":"F Neese","year":"2025","unstructured":"Neese, F.: Software update: the orca program system-version 6.0. Wiley Interdisc. Rev. Comput. Mol. Sci. 15(2), e70019 (2025)","journal-title":"Wiley Interdisc. Rev. Comput. Mol. Sci."},{"issue":"2","key":"21_CR24","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1021\/ct700248k","volume":"4","author":"J Tirado-Rives","year":"2008","unstructured":"Tirado-Rives, J., Jorgensen, W.L.: Performance of B3LYP density functional methods for a large set of organic molecules. J. Chem. Theory Comput. 4(2), 297\u2013306 (2008)","journal-title":"J. Chem. Theory Comput."},{"issue":"5","key":"21_CR25","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1002\/jcc.22885","volume":"33","author":"L Tian","year":"2012","unstructured":"Tian, L., Chen, F.: Multiwfn: a multifunctional wavefunction analyzer. J. Comput. Chem. 33(5), 580\u2013592 (2012)","journal-title":"J. Comput. Chem."},{"issue":"8","key":"21_CR26","doi-asserted-by":"publisher","first-page":"082503","DOI":"10.1063\/5.0216272","volume":"161","author":"T Lu","year":"2024","unstructured":"Lu, T.: A comprehensive electron wavefunction analysis toolbox for chemists. Multiwfn. J. Chem. Phys. 161(8), 082503 (2024)","journal-title":"Multiwfn. J. Chem. Phys."},{"issue":"D1","key":"21_CR27","doi-asserted-by":"publisher","first-page":"D1516","DOI":"10.1093\/nar\/gkae1059","volume":"53","author":"S Kim","year":"2025","unstructured":"Kim, S., et al.: PubChem 2025 update. Nucleic Acids Res. 53(D1), D1516\u2013D1525 (2025)","journal-title":"Nucleic Acids Res."},{"key":"21_CR28","unstructured":"World Health Organization, et al.: Collaborating Centre for drug statistics methodology, guidelines for ATC classification and DDD assignment. WHO Collaborating Centre for Drug Statistics Methodology, vol. 18 (2000)"},{"key":"21_CR29","doi-asserted-by":"crossref","unstructured":"Tatonetti, N.P., Ye, P.P., Daneshjou, R., Altman, R.B.: Data-driven prediction of drug effects and interactions. Sci. Transl. Med. 4(125), 125ra31\u2013125ra31 (2012)","DOI":"10.1126\/scitranslmed.3003377"},{"issue":"6088","key":"21_CR30","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1038\/323533a0","volume":"323","author":"DE Rumelhart","year":"1986","unstructured":"Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533\u2013536 (1986)","journal-title":"Nature"},{"key":"21_CR31","unstructured":"Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)"},{"issue":"10","key":"21_CR32","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.1002\/cmdc.200800178","volume":"3","author":"J Degen","year":"2008","unstructured":"Degen, J., Wegscheid-Gerlach, C., Zaliani, A., Rarey, M.: On the art of compiling and using \u2018drug-like\u2019 chemical fragment spaces. ChemMedChem 3(10), 1503 (2008)","journal-title":"ChemMedChem"},{"key":"21_CR33","unstructured":"Tan, M., Le, Q.: EfficientNetV2: smaller models and faster training. In: International Conference on Machine Learning, pp. 10096\u201310106. PMLR (2021)"},{"key":"21_CR34","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."}],"container-title":["Lecture Notes in Computer Science","Predictive Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-07904-6_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T05:48:10Z","timestamp":1763012890000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-07904-6_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,14]]},"ISBN":["9783032079039","9783032079046"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-07904-6_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,14]]},"assertion":[{"value":"14 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on PRedictive Intelligence In MEdicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","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":"27 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"prime2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/basira-lab.com\/prime-miccai-2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}