{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T14:52:55Z","timestamp":1769611975578,"version":"3.49.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,5,24]],"date-time":"2020-05-24T00:00:00Z","timestamp":1590278400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,5,24]],"date-time":"2020-05-24T00:00:00Z","timestamp":1590278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"publisher","award":["676434"],"award-info":[{"award-number":["676434"]}],"id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>For kinase inhibitors, X-ray crystallography has revealed different types of binding modes. Currently, more than 2000 kinase inhibitors with known binding modes are available, which makes it possible to derive and test machine learning models for the prediction of inhibitors with different binding modes. We have addressed this prediction task to evaluate and compare the information content of distinct molecular representations including protein\u2013ligand interaction fingerprints (IFPs) and compound structure-based structural fingerprints (i.e., atom environment\/fragment fingerprints). IFPs were designed to capture binding mode-specific interaction patterns at different resolution levels. Accurate predictions of kinase inhibitor binding modes were achieved with random forests using both representations. The performance of IFPs was consistently superior to atom environment fingerprints, albeit only by less than 10%. An active learning strategy applying information entropy-based selection of training instances was applied as a diagnostic approach to assess the relative information content of distinct representations. IFPs were found to capture more binding mode-relevant information than atom environment fingerprints, leading to highly predictive models even when training instances were randomly selected. By contrast, for atom environment fingerprints, the derivation of accurate models via active learning depended on entropy-based selection of informative training compounds. Notably, higher information content of IFPs confirmed by active learning only resulted in small improvements in global prediction accuracy compared to models derived using atom environment fingerprints. For practical applications, prediction of binding modes of new kinase inhibitors on the basis of chemical structure is highly attractive.<\/jats:p>","DOI":"10.1186\/s13321-020-00434-7","type":"journal-article","created":{"date-parts":[[2020,5,24]],"date-time":"2020-05-24T09:02:42Z","timestamp":1590310962000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Assessing the information content of structural and protein\u2013ligand interaction representations for the classification of kinase inhibitor binding modes via machine learning and active learning"],"prefix":"10.1186","volume":"12","author":[{"given":"Raquel","family":"Rodr\u00edguez-P\u00e9rez","sequence":"first","affiliation":[]},{"given":"Filip","family":"Miljkovi\u0107","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0557-5714","authenticated-orcid":false,"given":"J\u00fcrgen","family":"Bajorath","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,5,24]]},"reference":[{"key":"434_CR1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aan4368","author":"S Klaeger","year":"2017","unstructured":"Klaeger S, Heinzlmeir S, Wilheim M et al (2017) The target landscape of clinical kinase drugs. Science. https:\/\/doi.org\/10.1126\/science.aan4368","journal-title":"Science"},{"key":"434_CR2","doi-asserted-by":"publisher","first-page":"17295","DOI":"10.1021\/acsomega.8b02998","volume":"3","author":"F Miljkovi\u0107","year":"2018","unstructured":"Miljkovi\u0107 F, Bajorath J (2018) Computational analysis of kinase inhibitors identifies promiscuity cliffs across the human kinome. ACS Omega 3:17295\u201317308","journal-title":"ACS Omega"},{"key":"434_CR3","doi-asserted-by":"publisher","first-page":"FSO179","DOI":"10.4155\/fsoa-2017-0001","volume":"3","author":"Y Hu","year":"2017","unstructured":"Hu Y, Bajorath J (2017) Entering the \u2018big data\u2019 era in medicinal chemistry: molecular promiscuity analysis revisited. Future Sci 3:FSO179","journal-title":"Future Sci"},{"key":"434_CR4","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1021\/acsomega.7b01960","volume":"3","author":"F Miljkovi\u0107","year":"2018","unstructured":"Miljkovi\u0107 F, Bajorath J (2018) Exploring selectivity of multi-kinase inhibitors across the human kinome. ACS Omega 3:1147\u20131153","journal-title":"ACS Omega"},{"key":"434_CR5","doi-asserted-by":"publisher","first-page":"4367","DOI":"10.1021\/acsomega.9b00298","volume":"4","author":"R Rodr\u00edguez-P\u00e9rez","year":"2019","unstructured":"Rodr\u00edguez-P\u00e9rez R, Bajorath J (2019) Multi-task machine learning for classifying highly and weakly potent kinase inhibitors. ACS Omega 4:4367\u20134375","journal-title":"ACS Omega"},{"key":"434_CR6","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1021\/jm400378w","volume":"57","author":"OPJ van Linden","year":"2014","unstructured":"van Linden OPJ, Kooistra AJ, Leurs R, de Esch IJP, de Graaf C (2014) KLIFS: a knowledge-based structural database to navigate kinase-ligand interaction space. J Med Chem 57:249\u2013277","journal-title":"J Med Chem"},{"key":"434_CR7","doi-asserted-by":"publisher","first-page":"D365","DOI":"10.1093\/nar\/gkv1082","volume":"44","author":"J Kooistra","year":"2016","unstructured":"Kooistra J, Kanev GK, van Linden OPJ et al (2016) KLIFS: a structural kinase-ligand interaction database. Nucleic Acids Res 44:D365\u2013D371","journal-title":"Nucleic Acids Res"},{"key":"434_CR8","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1016\/j.drudis.2011.07.006","volume":"16","author":"S Kalyaanamoorthy","year":"2011","unstructured":"Kalyaanamoorthy S, Chen YP (2011) Structure-based drug design to augment hit discovery. Drug Discov Today 16:831\u2013839","journal-title":"Drug Discov Today"},{"key":"434_CR9","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.phrs.2015.10.021","volume":"103","author":"R Roskoski","year":"2016","unstructured":"Roskoski R (2016) Classification of small molecule protein kinase inhibitors based upon the structures of their drug-enzyme complexes. Pharmacol Res 103:26\u201348","journal-title":"Pharmacol Res"},{"key":"434_CR10","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1038\/nchembio.1938","volume":"11","author":"S M\u00fcller","year":"2015","unstructured":"M\u00fcller S, Chaikuad A, Gray NS, Knapp S (2015) The ins and outs of selective kinase inhibitor development. Nat Chem Biol 11:818\u2013821","journal-title":"Nat Chem Biol"},{"key":"434_CR11","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1021\/ci600342e","volume":"47","author":"G Marcou","year":"2007","unstructured":"Marcou G, Rognan D (2007) Optimizing fragment and scaffold docking by use of molecular interaction fingerprints. J Chem Inf Model 47:195\u2013207","journal-title":"J Chem Inf Model"},{"key":"434_CR12","doi-asserted-by":"publisher","first-page":"8195","DOI":"10.1021\/jm2011589","volume":"54","author":"C de Graaf","year":"2011","unstructured":"de Graaf C, Kooistra AJ, Vischer HF et al (2011) Crystal structure-based virtual screening for fragment-like ligands of the human histamine H1 receptor. J Med Chem 54:8195\u20138206","journal-title":"J Med Chem"},{"key":"434_CR13","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1021\/jm030331x","volume":"47","author":"Z Deng","year":"2004","unstructured":"Deng Z, Chuaqui C, Singh J (2004) Structural interaction fingerprint (SIFt): a novel method for analyzing three-dimensional protein-ligand binding interactions. J Med Chem 47:337\u2013344","journal-title":"J Med Chem"},{"key":"434_CR14","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s13321-018-0302-y","volume":"10","author":"A R\u00e1cz","year":"2018","unstructured":"R\u00e1cz A, Bajusz D, H\u00e9berger K (2018) Life beyond the Tanimoto coeffcient: similarity measures for interaction fingerprints. J Cheminform 10:48","journal-title":"J Cheminform"},{"key":"434_CR15","doi-asserted-by":"publisher","first-page":"2555","DOI":"10.1021\/ci500319f","volume":"54","author":"C Da","year":"2014","unstructured":"Da C, Kireev D (2014) Structural Protein-ligand interaction fingerprints (SPLIF) for structure-based virtual screening: method and benchmark study. J Chem Inf Model 54:2555\u20132561","journal-title":"J Chem Inf Model"},{"key":"434_CR16","doi-asserted-by":"publisher","first-page":"1942","DOI":"10.1021\/ci049870g","volume":"44","author":"MD Kelly","year":"2004","unstructured":"Kelly MD, Mancera RL (2004) Expanded interaction fingerprint method for analyzing ligand binding modes in docking and structure-based drug design. J Chem Inf Comput Sci 44:1942\u20131951","journal-title":"J Chem Inf Comput Sci"},{"key":"434_CR17","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1021\/ci300200r","volume":"53","author":"V Chupakhin","year":"2013","unstructured":"Chupakhin V, Marcou G, Baskin I, Varnek A, Rognan D (2013) Predicting ligand binding modes from neural networks trained on protein-ligand interaction fingerprints. J Chem Inf Model 53:763\u2013772","journal-title":"J Chem Inf Model"},{"key":"434_CR18","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jmedchem.9b00867","author":"F Miljkovi\u0107","year":"2019","unstructured":"Miljkovi\u0107 F, Rodr\u00edguez-P\u00e9rez R, Bajorath J (2019) Machine learning models for accurate prediction of kinase inhibitors with different binding modes. J Med Chem. https:\/\/doi.org\/10.1021\/acs.jmedchem.9b00867(in press)","journal-title":"J Med Chem"},{"key":"434_CR19","first-page":"763","volume":"52","author":"E Martin","year":"2012","unstructured":"Martin E, Mukherjee P (2012) Kinase-kernel models: accurate in silico screening of 4 million compounds across the entire human kinome. J Chem Inf Model 52:763\u2013772","journal-title":"J Chem Inf Model"},{"key":"434_CR20","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1021\/acs.jcim.6b00520","volume":"57","author":"N Bosc","year":"2017","unstructured":"Bosc N, Wroblowski B, Meyer C, Bonnet P (2017) Prediction of protein kinase\u2013ligand interactions through 2.5 D kinochemometrics. J Chem Inf Model 57:93\u2013101","journal-title":"J Chem Inf Model"},{"key":"434_CR21","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1038\/nchembio799","volume":"2","author":"Y Liu","year":"2006","unstructured":"Liu Y, Gray NS (2006) Rational design of inhibitors that bind to inactive kinase conformations. Nat Chem Biol 2:358\u2013364","journal-title":"Nat Chem Biol"},{"key":"434_CR22","doi-asserted-by":"publisher","first-page":"1230","DOI":"10.1021\/cb500129t","volume":"9","author":"Z Zhao","year":"2014","unstructured":"Zhao Z, Wu H, Wang L et al (2014) Exploration of type II binding mode: a privileged approach for kinase inhibitor focused drug discovery? ACS Chem Biol 9:1230\u20131241","journal-title":"ACS Chem Biol"},{"key":"434_CR23","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50:742\u2013754","journal-title":"J Chem Inf Model"},{"key":"434_CR24","unstructured":"OEChem TK, version 2.0.0; OpenEye Scientific Software, Santa Fe, NM"},{"key":"434_CR25","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332","journal-title":"Mach Learn"},{"key":"434_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1214\/aos\/1176344552","volume":"7","author":"B Efron","year":"1979","unstructured":"Efron B (1979) Bootstrap methods: another look at the Jackknife. Ann Stat 7:1\u201326","journal-title":"Ann Stat"},{"key":"434_CR27","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"434_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7","volume-title":"The elements of statistical learning","author":"T Hastie","year":"2009","unstructured":"Hastie T, Tibshirani R, Friedman JH (2009) The elements of statistical learning. Springer, Berlin"},{"key":"434_CR29","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1613\/jair.295","volume":"4","author":"DA Cohn","year":"1996","unstructured":"Cohn DA, Ghahramani Z, Jordan MI (1996) Active learning with statistical models. J Artif Intell Res 4:129\u2013145","journal-title":"J Artif Intell Res"},{"key":"434_CR30","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","volume":"27","author":"CE Shannon","year":"1948","unstructured":"Shannon CE (1948) A mathematical theory of communication. Bell Labs Tech J 27:379\u2013423","journal-title":"Bell Labs Tech J"},{"key":"434_CR31","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1016\/0005-2795(75)90109-9","volume":"405","author":"B Matthews","year":"1975","unstructured":"Matthews B (1975) Comparison of the predicted and observed secondary structure of t4 phage lysozyme. Biochim Biophys Acta 405:442\u2013451","journal-title":"Biochim Biophys Acta"},{"key":"434_CR32","doi-asserted-by":"crossref","unstructured":"Brodersen KH, Ong CS, Stephan KE, Buhmann JM (2010) The balanced accuracy and its posterior distribution. Proceedings of the 20th International Conference on Pattern Recognition (ICPR):3121-3124","DOI":"10.1109\/ICPR.2010.764"},{"key":"434_CR33","first-page":"1833","volume":"11","author":"M Ojala","year":"2010","unstructured":"Ojala M, Garriga G (2010) Permutation tests for studying classifier performance. J Mach Learn Res 11:1833\u20131863","journal-title":"J Mach Learn Res"},{"key":"434_CR34","first-page":"2579","volume":"9","author":"L Van der Maate","year":"2008","unstructured":"Van der Maate L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579\u20132605","journal-title":"J Mach Learn Res"},{"key":"434_CR35","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1214\/aoms\/1177729694","volume":"22","author":"S Kullback","year":"1951","unstructured":"Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22:79\u201386","journal-title":"Ann Math Stat"},{"key":"434_CR36","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1021\/ci9800211","volume":"38","author":"P Willet","year":"1998","unstructured":"Willet P, Barnard J, Downs G (1998) Chemical similarity searching. J Chem Inf Comp Sci 38:983\u2013996","journal-title":"J Chem Inf Comp Sci"},{"key":"434_CR37","unstructured":"https:\/\/zenodo.org\/record\/3743636"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-020-00434-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-020-00434-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-020-00434-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,23]],"date-time":"2021-05-23T23:27:40Z","timestamp":1621812460000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-020-00434-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,24]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["434"],"URL":"https:\/\/doi.org\/10.1186\/s13321-020-00434-7","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,24]]},"assertion":[{"value":"21 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 May 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing financial interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"36"}}