{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T02:20:55Z","timestamp":1783131655930,"version":"3.54.6"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2018,10,11]],"date-time":"2018-10-11T00:00:00Z","timestamp":1539216000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004063","name":"Knut och Alice Wallenbergs Stiftelse","doi-asserted-by":"publisher","award":["KAW: 2013.0253"],"award-info":[{"award-number":["KAW: 2013.0253"]}],"id":[{"id":"10.13039\/501100004063","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001862","name":"Svenska Forskningsr\u00e5det Formas","doi-asserted-by":"publisher","award":["FORMAS EDC-2020: 2016-0203"],"award-info":[{"award-number":["FORMAS EDC-2020: 2016-0203"]}],"id":[{"id":"10.13039\/501100001862","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1186\/s13321-018-0304-9","type":"journal-article","created":{"date-parts":[[2018,10,10]],"date-time":"2018-10-10T20:53:15Z","timestamp":1539204795000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Evaluating parameters for ligand-based modeling with random forest on sparse data sets"],"prefix":"10.1186","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5295-010X","authenticated-orcid":false,"given":"Alexander","family":"Kensert","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Alvarsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ulf","family":"Norinder","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ola","family":"Spjuth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,10,11]]},"reference":[{"issue":"3","key":"304_CR1","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1021\/mp100444g","volume":"8","author":"S Tian","year":"2011","unstructured":"Tian S, Li Y, Wang J, Zhang J, Hou T (2011) ADME evaluation in drug discovery. 9. Prediction of oral bioavailability in humans based on molecular properties and structural fingerprints. Mol Pharm 8(3):841\u2013851. \n                    https:\/\/doi.org\/10.1021\/mp100444g","journal-title":"Mol Pharm"},{"key":"304_CR2","doi-asserted-by":"publisher","first-page":"2271","DOI":"10.1093\/bioinformatics\/bty070","volume":"34","author":"J Wu","year":"2018","unstructured":"Wu J, Zhang Q, Wu W, Pang T, Hu H, Chan WKB (2018) WDL-RF: predicting bioactivities of ligand molecules acting with G protein-coupled receptors by combining weighted deep learning and random forest. Bioinformatics 34:2271\u20132282. \n                    https:\/\/doi.org\/10.1093\/bioinformatics\/bty070","journal-title":"Bioinformatics"},{"issue":"1","key":"304_CR3","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.tiv.2008.09.017","volume":"23","author":"H Zhang","year":"2009","unstructured":"Zhang H, Chen QY, Xiang ML, Ma CY, Huang Q, Yang SY (2009) In silico prediction of mitochondrial toxicity by using GA-CG-SVM approach. Toxicol in Vitro 23(1):134\u2013140","journal-title":"Toxicol in Vitro"},{"key":"304_CR4","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1111\/j.1747-0285.2012.01411.x","volume":"80","author":"E Myshkin","year":"2012","unstructured":"Myshkin E, Brennan R, Khasanova T, Sitnik T, Serebriyskaya T, Litvinova E (2012) Prediction of organ toxicity endpoints by QSAR modeling based on precise chemical-histopathology annotations. Chem Biol Drug Des 80:406\u2013416","journal-title":"Chem Biol Drug Des"},{"issue":"8","key":"304_CR5","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1021\/tx200148a","volume":"24","author":"Y Low","year":"2011","unstructured":"Low Y, Uehara T, Minowa Y, Yamada H, Ohno Y, Urushidani T (2011) Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches. Chem Res Toxicol 24(8):1251\u20131262. \n                    https:\/\/doi.org\/10.1021\/tx200148a","journal-title":"Chem Res Toxicol"},{"issue":"11","key":"304_CR6","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1021\/ci900203n","volume":"49","author":"PG Polishchuk","year":"2009","unstructured":"Polishchuk PG, Muratov EN, Artemenko AG, Kolumbin OG, Muratov NN, Kuz\u2019min VE (2009) Application of random forest approach to QSAR prediction of aquatic toxicity. J Chem Inf Model 49(11):2481\u20132488. \n                    https:\/\/doi.org\/10.1021\/ci900203n","journal-title":"J Chem Inf Model"},{"issue":"5","key":"304_CR7","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(5):742\u2013754","journal-title":"J Chem Inf Model"},{"issue":"3","key":"304_CR8","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1021\/jm060333s","volume":"50","author":"BF Jensen","year":"2007","unstructured":"Jensen BF, Vind C, Brockhoff PB, Refsgaard HHF (2007) In silico prediction of cytochrome P450 2D6 and 3A4 inhibition using Gaussian kernel weighted k-nearest neighbor and extended connectivity fingerprints, including structural fragment analysis of inhibitors versus noninhibitors. J Med Chem 50(3):501\u2013511. \n                    https:\/\/doi.org\/10.1021\/jm060333s","journal-title":"J Med Chem"},{"issue":"7","key":"304_CR9","doi-asserted-by":"publisher","first-page":"682","DOI":"10.1177\/1087057105281365","volume":"10","author":"D Rogers","year":"2005","unstructured":"Rogers D, Brown RD, Hahn M (2005) Using extended-connectivity fingerprints with Laplacian\u2013modified Bayesian analysis in high-throughput screening follow-up. J Biomol Screen 10(7):682\u2013686. \n                    https:\/\/doi.org\/10.1177\/1087057105281365","journal-title":"J Biomol Screen"},{"issue":"5","key":"304_CR10","doi-asserted-by":"publisher","first-page":"981","DOI":"10.1021\/ci800024c","volume":"48","author":"D Zhou","year":"2008","unstructured":"Zhou D, Alelyunas Y, Liu R (2008) Scores of extended connectivity fingerprint as descriptors in QSPR study of melting point and aqueous solubility. J Chem Inf Model 48(5):981\u2013987. \n                    https:\/\/doi.org\/10.1021\/ci800024c","journal-title":"J Chem Inf Model"},{"issue":"4","key":"304_CR11","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1021\/ci049965i","volume":"44","author":"XJ Yao","year":"2004","unstructured":"Yao XJ, Panaye A, Doucet JP, Zhang RS, Chen HF, Liu MC (2004) Comparative study of QSAR\/QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression. J Chem Inf Comput Sci 44(4):1257\u20131266. \n                    https:\/\/doi.org\/10.1021\/ci049965i","journal-title":"J Chem Inf Comput Sci"},{"issue":"8","key":"304_CR12","doi-asserted-by":"publisher","first-page":"1576","DOI":"10.1021\/acs.jcim.6b00136","volume":"56","author":"I Cortes-Ciriano","year":"2016","unstructured":"Cortes-Ciriano I (2016) Benchmarking the predictive power of ligand efficiency indices in QSAR. J Chem Inf Model 56(8):1576\u20131587. \n                    https:\/\/doi.org\/10.1021\/acs.jcim.6b00136","journal-title":"J Chem Inf Model"},{"issue":"1","key":"304_CR13","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1016\/S0925-2312(03)00374-6","volume":"55","author":"U Norinder","year":"2003","unstructured":"Norinder U (2003) Support vector machine models in drug design: applications to drug transport processes and QSAR using simplex optimisations and variable selection. Neurocomputing 55(1):337\u2013346","journal-title":"Neurocomputing"},{"issue":"9","key":"304_CR14","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1007\/s00706-011-0493-7","volume":"142","author":"XB Zhou","year":"2011","unstructured":"Zhou XB, Han WJ, Chen J, Lu XQ (2011) QSAR study on the interactions between antibiotic compounds and DNA by a hybrid genetic-based support vector machine. Monatshefte fuer Chemie\/Chemical Monthly 142(9):949\u2013959. \n                    https:\/\/doi.org\/10.1007\/s00706-011-0493-7","journal-title":"Monatshefte fuer Chemie\/Chemical Monthly"},{"issue":"1","key":"304_CR15","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(1):5\u201332","journal-title":"Mach Learn"},{"issue":"11","key":"304_CR16","doi-asserted-by":"publisher","first-page":"2551","DOI":"10.1021\/ci9002206","volume":"49","author":"L Carlsson","year":"2009","unstructured":"Carlsson L, Helgee EA, Boyer S (2009) Interpretation of nonlinear QSAR models applied to Ames mutagenicity data. J Chem Inf Model 49(11):2551\u20132558. \n                    https:\/\/doi.org\/10.1021\/ci9002206","journal-title":"J Chem Inf Model"},{"issue":"6","key":"304_CR17","doi-asserted-by":"publisher","first-page":"2369","DOI":"10.1021\/ci0601160","volume":"46","author":"EO Cannon","year":"2006","unstructured":"Cannon EO, Bender A, Palmer DS, Mitchell JBO (2006) Chemoinformatics-based classification of prohibited substances employed for doping in sport. J Chem Inf Model 46(6):2369\u20132380. \n                    https:\/\/doi.org\/10.1021\/ci0601160","journal-title":"J Chem Inf Model"},{"issue":"2","key":"304_CR18","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1186\/s12911-016-0309-0","volume":"16","author":"A Henriksson","year":"2016","unstructured":"Henriksson A, Zhao J, Dalianis H, Bostr\u00f6m H (2016) Ensembles of randomized trees using diverse distributed representations of clinical events. BMC Med Inf Decis Mak 16(2):69. \n                    https:\/\/doi.org\/10.1186\/s12911-016-0309-0","journal-title":"BMC Med Inf Decis Mak"},{"key":"304_CR19","doi-asserted-by":"crossref","unstructured":"Karlsson I, Bostr\u00f6m H (2014) Handling sparsity with random forests when predicting adverse drug events from electronic health records. In: 2014 ieee international conference on healthcare informatics, 15\u201317 September 2014, Verona. IEEE, pp 17\u201322","DOI":"10.1109\/ICHI.2014.10"},{"key":"304_CR20","unstructured":"Svetnik V, Liaw A, Tong C, Wang T (2004) Multiple classifier systems. In: Proceedings. Springer, Berlin"},{"issue":"6","key":"304_CR21","doi-asserted-by":"publisher","first-page":"1947","DOI":"10.1021\/ci034160g","volume":"43","author":"V Svetnik","year":"2003","unstructured":"Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43(6):1947\u20131958. \n                    https:\/\/doi.org\/10.1021\/ci034160g","journal-title":"J Chem Inf Comput Sci"},{"issue":"2","key":"304_CR22","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1021\/c160017a018","volume":"5","author":"HL Morgan","year":"1965","unstructured":"Morgan HL (1965) The generation of a unique machine description for chemical structures: a technique developed at chemical abstracts service. J Chem Doc 5(2):107\u2013113. \n                    https:\/\/doi.org\/10.1021\/c160017a018","journal-title":"J Chem Doc"},{"issue":"1","key":"304_CR23","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.bmcl.2012.10.102","volume":"23","author":"U Norinder","year":"2013","unstructured":"Norinder U, Ek ME (2013) QSAR investigation of NaV1.7 active compounds using the SVM\/signature approach and the bioclipse modeling platform. Bioorg Med Chem Lett 23(1):261\u2013263","journal-title":"Bioorg Med Chem Lett"},{"key":"304_CR24","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.ces.2016.02.037","volume":"159","author":"JJF Chen","year":"2017","unstructured":"Chen JJF, Visco DP Jr (2017) Developing an in silico pipeline for faster drug candidate discovery: virtual high throughput screening with the signature molecular descriptor using support vector machine models. Chem Eng Sci 159:31\u201342","journal-title":"Chem Eng Sci"},{"key":"304_CR25","doi-asserted-by":"publisher","first-page":"85","DOI":"10.3389\/fenvs.2015.00085","volume":"3","author":"R Huang","year":"2016","unstructured":"Huang R, Xia M, Nguyen D-T, Zhao T, Sakamuru S, Zhao J, Shahane SA, Rossoshek A, Simeonov A (2016) Tox21 challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs. Front Environ Sci 3:85","journal-title":"Front Environ Sci"},{"issue":"9","key":"304_CR26","doi-asserted-by":"publisher","first-page":"2077","DOI":"10.1021\/ci900161g","volume":"49","author":"K Hansen","year":"2009","unstructured":"Hansen K, Mika S, Schroeter T, Sutter A, ter Laak A, Steger-Hartmann T (2009) Benchmark data set for in silico prediction of Ames mutagenicity. J Chem Inf Model 49(9):2077\u20132081","journal-title":"J Chem Inf Model"},{"issue":"3","key":"304_CR27","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/MCSE.2007.55","volume":"9","author":"JD Hunter","year":"2007","unstructured":"Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Sci Eng 9(3):90\u201395","journal-title":"Comput Sci Eng"},{"key":"304_CR28","doi-asserted-by":"publisher","unstructured":"Waskom M, Botvinnik O, O\u2019Kane D, Hobson P, Lukauskas S, Gemperline DC et\u00a0al (2017) mwaskom\/seaborn: v0.8.1. \n                    https:\/\/doi.org\/10.5281\/zenodo.54844","DOI":"10.5281\/zenodo.54844"},{"key":"304_CR29","unstructured":"Landrum G (2017) RDKit documentation 2017.09.01 release. \n                    http:\/\/www.rdkit.org\/RDKit_Docs.current.pdf\n                    \n                  . Accessed 15 Nov 2017"},{"key":"304_CR30","unstructured":"CPSign (2008). \n                    http:\/\/cpsign-docs.genettasoft.com\n                    \n                  . Accessed 04 June 2018"},{"key":"304_CR31","volume-title":"Classification and regression trees","author":"L Breiman","year":"1984","unstructured":"Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. Chapman and Hall, London"},{"key":"304_CR32","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/978-3-642-14831-6_25","volume-title":"Advanced intelligent computing theories and applications","author":"S Bernard","year":"2010","unstructured":"Bernard S, Heutte L, Adam S (2010) A study of strength and correlation in random forests. In: Huang DS, McGinnity M, Heutte L, Zhang XP (eds) Advanced intelligent computing theories and applications. Springer, Berlin, pp 186\u2013191"},{"issue":"1","key":"304_CR33","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1023\/B:AMAI.0000018580.96245.c6","volume":"41","author":"LE Raileanu","year":"2004","unstructured":"Raileanu LE, Stoffel K (2004) Theoretical comparison between the Gini index and information gain criteria. Ann Math Artif Intell 41(1):77\u201393. \n                    https:\/\/doi.org\/10.1023\/B:AMAI.0000018580.96245.c6","journal-title":"Ann Math Artif Intell"},{"key":"304_CR34","unstructured":"Karampatziakis N (2008) Fast ensembles of sparse trees. \n                    http:\/\/lowrank.net\/nikos\/fest\/\n                    \n                  . Accessed 15 Nov 2017"},{"key":"304_CR35","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"issue":"3","key":"304_CR36","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1145\/1961189.1961199","volume":"2","author":"CC Chang","year":"2011","unstructured":"Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27","journal-title":"ACM Trans Intell Syst Technol"},{"issue":"7","key":"304_CR37","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","volume":"30","author":"AP Bradley","year":"1997","unstructured":"Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 30(7):1145\u20131159","journal-title":"Pattern Recognit"},{"issue":"1","key":"304_CR38","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","volume":"143","author":"JA Hanley","year":"1982","unstructured":"Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29\u201336. \n                    https:\/\/doi.org\/10.1148\/radiology.143.1.7063747","journal-title":"Radiology"},{"issue":"5","key":"304_CR39","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1021\/ci100062n","volume":"50","author":"M Sastry","year":"2010","unstructured":"Sastry M, Lowrie JF, Dixon SL, Sherman W (2010) Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments. J Chem Inf Model 50(5):771\u201384","journal-title":"J Chem Inf Model"},{"issue":"8","key":"304_CR40","doi-asserted-by":"publisher","first-page":"1840","DOI":"10.1021\/ci200242c","volume":"51","author":"O Spjuth","year":"2011","unstructured":"Spjuth O, Eklund M, Ahlberg Helgee E, Boyer S, Carlsson L (2011) Integrated decision support for assessing chemical liabilities. J Chem Inf Model 51(8):1840\u20137","journal-title":"J Chem Inf Model"},{"key":"304_CR41","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1007\/978-3-319-17091-6_27","volume-title":"Statistical Learning and Data Sciences","author":"Ernst Ahlberg","year":"2015","unstructured":"Ahlberg E, Spjuth O, Hasselgren C, Carlsson L (2015) Interpretation of conformal prediction classification models. In: International symposium on statistical learning and data sciences. Springer, Berlin, pp 323\u2013334"},{"issue":"1","key":"304_CR42","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s13321-018-0271-1","volume":"10","author":"M Lapins","year":"2018","unstructured":"Lapins M, Arvidsson S, Lampa S, Berg A, Schaal W, Alvarsson J (2018) A confidence predictor for logD using conformal regression and a support-vector machine. J Cheminform 10(1):17","journal-title":"J Cheminform"},{"issue":"11","key":"304_CR43","doi-asserted-by":"publisher","first-page":"3211","DOI":"10.1021\/ci500344v","volume":"54","author":"J Alvarsson","year":"2014","unstructured":"Alvarsson J, Eklund M, Andersson C, Carlsson L, Spjuth O, Wikberg JES (2014) Benchmarking study of parameter variation when using signature fingerprints together with support vector machines. J Chem Inf Model 54(11):3211\u20133217. \n                    https:\/\/doi.org\/10.1021\/ci500344v","journal-title":"J Chem Inf Model"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-018-0304-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13321-018-0304-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-018-0304-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T19:03:53Z","timestamp":1570734233000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-018-0304-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,11]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,12]]}},"alternative-id":["304"],"URL":"https:\/\/doi.org\/10.1186\/s13321-018-0304-9","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10,11]]},"assertion":[{"value":"17 May 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"49"}}