{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T07:06:49Z","timestamp":1783235209307,"version":"3.54.6"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032093172","type":"print"},{"value":"9783032093189","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T00:00:00Z","timestamp":1762646400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T00:00:00Z","timestamp":1762646400000},"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\/978-3-032-09318-9_30","type":"book-chapter","created":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T06:22:33Z","timestamp":1762582953000},"page":"434-448","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Application of\u00a0Whitebox Machine Learning Models for\u00a0Optimizing Electrochemical Assays of\u00a0Drug Permeability"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9259-6831","authenticated-orcid":false,"given":"Uttaran","family":"Bera","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6914-5297","authenticated-orcid":false,"given":"Nirod Kumar","family":"Sarangi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4604-5533","authenticated-orcid":false,"given":"Tia E.","family":"Keyes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1329-2570","authenticated-orcid":false,"given":"Mark","family":"Roantree","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,9]]},"reference":[{"key":"30_CR1","doi-asserted-by":"crossref","unstructured":"Bibi, H.A., Di Cagno, M., Holm, R., Bauer-Brandl, A.: Permeapad\u2122for investigation of passive drug permeability: the effect of surfactants, co-solvents and simulated intestinal fluids (FaSSIF and FeSSIF). Int. J. Pharm. 493(1\u20132), 192\u2013197 (2015). ISSN 0378-5173, https:\/\/doi.org\/10.1016\/j.ijpharm.2015.07.028","DOI":"10.1016\/j.ijpharm.2015.07.028"},{"key":"30_CR2","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding arXiv:1810.04805 [cs.CL] 2018, https:\/\/doi.org\/10.48550\/arXiv.1810.04805"},{"key":"30_CR3","doi-asserted-by":"publisher","unstructured":"Chen, G., Shen, Z., Li, Y.: A machine-learning-assisted study of the permeability of small drug-like molecules across lipid membranes. Phys. Chem. Chem. Phys. 22(35), 19687\u201319696. The Royal Society of Chemistry (2020). https:\/\/doi.org\/10.1039\/D0CP03243C","DOI":"10.1039\/D0CP03243C"},{"key":"30_CR4","doi-asserted-by":"publisher","unstructured":"Loyola-Gonz\u00e1lez, O.: Black-box vs. white-box: understanding their advantages and weaknesses from a practical point of view. IEEE Access 7, 154096\u2013154113 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2949286","DOI":"10.1109\/ACCESS.2019.2949286"},{"key":"30_CR5","doi-asserted-by":"publisher","unstructured":"Huang, E.T.C., Yang, J.S., Liao, K.Y.K., et al.: Predicting blood\u2013brain barrier permeability of molecules with a large language model and machine learning. Sci. Rep. 14, 15844 (2024). https:\/\/doi.org\/10.1038\/s41598-024-66897-y","DOI":"10.1038\/s41598-024-66897-y"},{"key":"30_CR6","doi-asserted-by":"publisher","unstructured":"Jia, H., Sosso, G.C.: Transparent machine learning model to understand drug permeability through the blood-brain barrier. J. Chem. Inf. Model. 64(23), 8718\u20138728 (2024). https:\/\/doi.org\/10.1021\/acs.jcim.4c01217. Epub 2024 Nov 18. PMID: 39558528; PMCID: PMC11632763","DOI":"10.1021\/acs.jcim.4c01217"},{"key":"30_CR7","doi-asserted-by":"publisher","unstructured":"Kaur, H., et al.: Large area fabrication of semiconducting phosphorene by langmuir-blodgett assembly. Sci Rep. 27(6), 34095 (2016). https:\/\/doi.org\/10.1038\/srep34095. PMID: 27671093; PMCID: PMC5037434","DOI":"10.1038\/srep34095"},{"key":"30_CR8","doi-asserted-by":"publisher","unstructured":"Lazanas, A.C., Prodromidis, M.I.: Electrochemical impedance spectroscopy - a tutorial. ACS Meas. Sci. 3(3), 162\u2013193 (2023). https:\/\/doi.org\/10.1021\/acsmeasuresciau.2c00070","DOI":"10.1021\/acsmeasuresciau.2c00070"},{"key":"30_CR9","doi-asserted-by":"publisher","unstructured":"Leon, F.: A neuro-symbolic classification algorithm using neural cell assemblies. In: Nguyen, N.T., et al. (eds.) Advances in Computational Collective Intelligence. ICCCI 2024. CCIS, vol. 2166, pp. 247\u2013259. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-70259-4_19","DOI":"10.1007\/978-3-031-70259-4_19"},{"key":"30_CR10","doi-asserted-by":"publisher","unstructured":"Love, B.C., Medin, D.L., Gureckis, T.M.: SUSTAIN: a network model of category learning. Psychol. Rev. 111(2), 309\u2013332 (2004). https:\/\/doi.org\/10.1037\/0033-295x.111.2.309","DOI":"10.1037\/0033-295x.111.2.309"},{"key":"30_CR11","doi-asserted-by":"publisher","unstructured":"Radchenko, E.V., Antonyan, G.V., Ignatov, S.K., Palyulin, V.A.: Machine learning prediction of mycobacterial cell wall permeability of drugs and drug-like compounds. Molecules 28, 633 (2023). https:\/\/doi.org\/10.3390\/molecules28020633","DOI":"10.3390\/molecules28020633"},{"key":"30_CR12","doi-asserted-by":"publisher","unstructured":"Ramadurai, S., et al.: Microcavity-supported lipid bilayers. Evaluation of drug-lipid membrane interactions by electrochemical impedance and fluorescence correlation spectroscopy. Langmuir 35, 8095\u20138109 (2019). https:\/\/doi.org\/10.1021\/acs.langmuir.9b01028","DOI":"10.1021\/acs.langmuir.9b01028"},{"key":"30_CR13","doi-asserted-by":"publisher","unstructured":"Tim R\u00e4z, M.L.: Interpretability: simple isn\u2019t easy. Stud. Hist. Philos. Sci. 103, 159\u2013167 (2024). ISSN 0039-3681, https:\/\/doi.org\/10.1016\/j.shpsa.2023.12.007","DOI":"10.1016\/j.shpsa.2023.12.007"},{"key":"30_CR14","doi-asserted-by":"publisher","unstructured":"Sarangi, N.K., Prabhakaran, A., Keyes, T.E.: Interaction of miltefosine with microcavity supported lipid membrane: biophysical insights from electrochemical impedance spectroscopy. Electroanalysis 32, 2936\u20132945 (2020). https:\/\/doi.org\/10.1002\/elan.202060424","DOI":"10.1002\/elan.202060424"},{"key":"30_CR15","doi-asserted-by":"publisher","unstructured":"Sarangi, N.K., Prabhakaran, A., Roantree, M., Keyes, T.E.: Evaluation of the passive permeability of antidepressants through pore-suspended lipid bilayer. Colloids Surfaces B Biointerfaces 234, 113688 (2024). https:\/\/doi.org\/10.1016\/j.colsurfb.2023.113688","DOI":"10.1016\/j.colsurfb.2023.113688"},{"key":"30_CR16","doi-asserted-by":"crossref","unstructured":"Sterling, T., Irwin, J.J.: ZINC 15 \u2013 ligand discovery for everyone. J. Chem. Inf. Model. 55(11) (2015)","DOI":"10.1021\/acs.jcim.5b00559"},{"issue":"4","key":"30_CR17","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1145\/3490699","volume":"65","author":"VC Storey","year":"2022","unstructured":"Storey, V.C., Lukyanenko, R., Maass, W., Parsons, J.: Explainable AI. Commun. ACM 65(4), 27\u201329 (2022)","journal-title":"Commun. ACM"},{"key":"30_CR18","doi-asserted-by":"publisher","unstructured":"Sun, D., Gao, W., Hu, H., Zhou, S.: Why 90% of clinical drug development fails and how to improve it? Acta Pharm. Sin. B 12(7), 3049\u20133062 (2022). https:\/\/doi.org\/10.1016\/j.apsb.2022.02.002. Epub 2022 Feb 11. PMID: 35865092; PMCID: PMC9293739","DOI":"10.1016\/j.apsb.2022.02.002"},{"issue":"1","key":"30_CR19","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. J. Chem. Inf. Comput. Sci. 28(1), 31\u201336 (1988)","journal-title":"J. Chem. Inf. Comput. Sci."}],"container-title":["Lecture Notes in Computer Science","Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-09318-9_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T05:02:30Z","timestamp":1768280550000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-09318-9_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,9]]},"ISBN":["9783032093172","9783032093189"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-09318-9_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,9]]},"assertion":[{"value":"9 November 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"All code, data, experimental results and decision tree rulebase and statistics are available upon reasonable request.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Supplementary Materials"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ho Chi Minh City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","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":"12 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}