{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:34:23Z","timestamp":1742913263337,"version":"3.40.3"},"publisher-location":"Cham","reference-count":8,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030304928"},{"type":"electronic","value":"9783030304935"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Four datasets measuring DMPK (drug metabolism and pharmacokinetics) parameters, and one target protein-specific dataset were analyzed by machine learning methods. Parameters measured for the five compound sets were biological activity data, plasma protein binding, permeability in MDCK I cell layers, intrinsic clearance by human liver microsomes, and plasma exposure in orally dosed rats. The measured data were sorted chronologically, reflecting the order in which they had been obtained in the discovery project. Subsets of the chronologically sorted data that appeared early in the project were used as training datasets to build predictive models for subsequent compounds based on kNN, partial least squares regression (PLSR), nonlinear PLSR, random forest regression, and support vector regression. A median model was used as a baseline to assess the machine learning model prediction quality. Data sets sorted in order of increasing test set prediction error: intrinsic clearance, plasma protein binding, cell layer permeability, biological activity on target protein, and bioavailability as AUC in rats. Our results give a first estimation of the power of machine learning to predict DMPK properties of compounds in an ongoing drug discovery project.<\/jats:p>","DOI":"10.1007\/978-3-030-30493-5_67","type":"book-chapter","created":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T20:03:41Z","timestamp":1568145821000},"page":"741-746","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predictive Power of Time-Series Based Machine Learning Models for DMPK Measurements in Drug Discovery"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2549-8672","authenticated-orcid":false,"given":"Modest","family":"von Korff","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2728-3699","authenticated-orcid":false,"given":"Olivier","family":"Corminboeuf","sequence":"additional","affiliation":[]},{"given":"John","family":"Gatfield","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7476-3340","authenticated-orcid":false,"given":"S\u00e9bastien","family":"Jeay","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5804-9072","authenticated-orcid":false,"given":"Isabelle","family":"Reymond","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4019-1959","authenticated-orcid":false,"given":"Thomas","family":"Sander","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"67_CR1","doi-asserted-by":"publisher","first-page":"4977","DOI":"10.1021\/jm4004285","volume":"57","author":"A Cherkasov","year":"2014","unstructured":"Cherkasov, A., et al.: Qsar modeling: where have you been? where are you going to? J. Med. Chem. 57, 4977\u20135010 (2014). https:\/\/doi.org\/10.1021\/jm4004285","journal-title":"J. Med. Chem."},{"key":"67_CR2","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1002\/qsar.200610151","volume":"26","author":"P Gramatica","year":"2007","unstructured":"Gramatica, P.: Principles of QSAR models validation: internal and external. QSAR Comb. Sci. 26, 694\u2013701 (2007). https:\/\/doi.org\/10.1002\/qsar.200610151","journal-title":"QSAR Comb. Sci."},{"key":"67_CR3","doi-asserted-by":"publisher","first-page":"667","DOI":"10.2533\/chimia.2017.667","volume":"71","author":"C Boss","year":"2017","unstructured":"Boss, C., et al.: The screening compound collection: a key asset for drug discovery. Chimia (Aarau) 71, 667\u2013677 (2017). https:\/\/doi.org\/10.2533\/chimia.2017.667","journal-title":"Chimia (Aarau)"},{"key":"67_CR4","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/0169-7439(93)85002-X","volume":"18","author":"S de Jong","year":"1993","unstructured":"de Jong, S.: SIMPLS: an alternative approach to partial least squares regression. Chemometr. Intell. Lab. Syst. 18, 251\u2013263 (1993). https:\/\/doi.org\/10.1016\/0169-7439(93)85002-X","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"67_CR5","doi-asserted-by":"publisher","first-page":"169","DOI":"10.2307\/2348250","volume":"41","author":"R Sakia","year":"1992","unstructured":"Sakia, R.: The box-cox transformation technique: a review. J. Roy. Stat. Soc.: Ser. D (Stat.) 41, 169\u2013178 (1992). https:\/\/doi.org\/10.2307\/2348250","journal-title":"J. Roy. Stat. Soc.: Ser. D (Stat.)"},{"unstructured":"https:\/\/haifengl.github.io\/smile\/. Accessed 02 July 2019","key":"67_CR6"},{"issue":"3","key":"67_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"Chih-Chung Chang","year":"2011","unstructured":"Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2, 27 (2011). https:\/\/doi.org\/10.1145\/1961189.1961199","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"67_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0169-7439(00)00122-2","volume":"56","author":"Q-S Xu","year":"2001","unstructured":"Xu, Q.-S., Liang, Y.-Z.: Monte carlo cross validation. Chemometr. Intell. Lab. Syst. 56, 1\u201311 (2001). https:\/\/doi.org\/10.1016\/S0169-7439(00)00122-2","journal-title":"Chemometr. Intell. Lab. Syst."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Workshop and Special Sessions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30493-5_67","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T16:54:57Z","timestamp":1710348897000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-30493-5_67"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030304928","9783030304935"],"references-count":8,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30493-5_67","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"9 September 2019","order":1,"name":"first_online","label":"First Online","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":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}