title: DemQSAR: predicting human volume of distribution and clearance of drugs.
abstract: In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure-activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds. In many cases the number of generated features exceeds the number of molecular compounds with known property values that are available for learning. Machine learning methods tend to overfit the training data in such situations, i.e. the method adjusts to very specific features of the training data, which are not characteristic for the considered property. This problem can be alleviated by diminishing the influence of unimportant, redundant or even misleading features. A better strategy is to eliminate such features completely. Ideally, a molecular property can be described by a small number of features that are chemically interpretable. The purpose of the present contribution is to provide a predictive modeling approach, which combines feature generation, feature selection, model building and control of overtraining into a single application called DemQSAR. DemQSAR is used to predict human volume of distribution (VD(ss)) and human clearance (CL). To control overtraining, quadratic and linear regularization terms were employed. A recursive feature selection approach is used to reduce the number of descriptors. The prediction performance is as good as the best predictions reported in the recent literature. The example presented here demonstrates that DemQSAR can generate a model that uses very few features while maintaining high predictive power. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human VD(ss) and CL is available on the webpage of DemPRED:                             http://agknapp.chemie.fu-berlin.de/dempred/                                                     .
authors.affiliation: Institute of Chemistry and Biochemistry, Freie Universität Berlin, Fabeckstrasse 36A, 14195, Berlin, Germany. o.demir@fu-berlin.de
authors.forename: Ozgur
authors.initials: O
authors.lastname: Demir-Kavuk
authors.forename: Jörg
authors.initials: J
authors.lastname: Bentzien
authors.forename: Ingo
authors.initials: I
authors.lastname: Muegge
authors.forename: Ernst-Walter
authors.initials: EW
authors.lastname: Knapp
mesh_terms: D001185:Artificial Intelligence; D006801:Humans; D008657:Metabolic Clearance Rate; D008954:Models, Biological; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D010599:Pharmacokinetics; D021281:Quantitative Structure-Activity Relationship
chemical_list: D004364:Pharmaceutical Preparations
references.citation: Curr Top Med Chem. 2011;11(4):358-81
references.pmid: 21320065
references.citation: Drug Metab Dispos. 2010 Nov;38(11):2083-90
references.pmid: 20693417
references.citation: SAR QSAR Environ Res. 2009;20(3-4):241-66
references.pmid: 19544191
references.citation: Curr Top Med Chem. 2008;8(18):1555-72
references.pmid: 19075767
references.citation: Drug Metab Dispos. 1999 Nov;27(11):1350-9
references.pmid: 10534321
references.citation: Drug Metab Dispos. 2009 May;37(5):1025-34
references.pmid: 19196847
references.citation: J Comput Aided Mol Des. 2004 Jul-Sep;18(7-9):437-49
references.pmid: 15729845
references.citation: Curr Med Chem. 2009;16(2):189-202
references.pmid: 19149571
references.citation: J Mol Graph Model. 2002 Jan;20(4):269-76
references.pmid: 11858635
references.citation: J Mol Graph Model. 2010 Jun;28(8):899-903
references.pmid: 20427217
references.citation: Drug Metab Dispos. 2008 Jul;36(7):1385-405
references.pmid: 18426954
references.citation: J Pharmacol Exp Ther. 1997 Oct;283(1):46-58
references.pmid: 9336307
references.citation: Nat Rev Drug Discov. 2004 Aug;3(8):711-5
references.pmid: 15286737
references.citation: Toxicol Sci. 2010 Oct;117(2):348-58
references.pmid: 20639261
references.citation: Eur J Pharm Sci. 2000 Oct;11 Suppl 2:S29-39
references.pmid: 11033425
references.citation: J Chem Inf Model. 2007 Mar-Apr;47(2):626-34
references.pmid: 17381177
references.citation: Curr Pharm Des. 2006;12(17):2111-20
references.pmid: 16796559
references.citation: Nat Rev Drug Discov. 2010 Mar;9(3):203-14
references.pmid: 20168317
references.citation: J Med Chem. 2009 Jul 23;52(14):4488-95
references.pmid: 19603833
references.citation: J Chem Inf Model. 2007 Nov-Dec;47(6):2345-57
references.pmid: 17880194
references.citation: J Chem Inf Comput Sci. 2003 Mar-Apr;43(2):493-500
references.pmid: 12653513
references.citation: Drug Metab Dispos. 2002 Dec;30(12):1446-54
references.pmid: 12433818
references.citation: J Chem Inf Model. 2005 Sep-Oct;45(5):1376-84
references.pmid: 16180914
references.citation: J Med Chem. 2006 Apr 6;49(7):2262-7
references.pmid: 16570922
references.citation: Nat Rev Drug Discov. 2011 Mar;10(3):197-208
references.pmid: 21358739
references.citation: Mol Inform. 2010 Jul 12;29(6-7):476-88
references.pmid: 27463326
references.citation: Biostatistics. 2004 Jul;5(3):427-43
references.pmid: 15208204
references.citation: J Chem Inf Model. 2010 Jul 26;50(7):1284-95
references.pmid: 20617831
references.citation: Biotechnol Bioeng. 2007 Jun 1;97(2):389-96
references.pmid: 17013940
references.citation: Proc IEEE Comput Soc Bioinform Conf. 2003;2:220-8
references.pmid: 16452796