title: The influence of the inactives subset generation on the performance of machine learning methods.
abstract: BACKGROUND
  A growing popularity of machine learning methods application in virtual screening, in both classification and regression tasks, can be observed in the past few years. However, their effectiveness is strongly dependent on many different factors.
  
  
  RESULTS
  In this study, the influence of the way of forming the set of inactives on the classification process was examined: random and diverse selection from the ZINC database, MDDR database and libraries generated according to the DUD methodology. All learning methods were tested in two modes: using one test set, the same for each method of inactive molecules generation and using test sets with inactives prepared in an analogous way as for training. The experiments were carried out for 5 different protein targets, 3 fingerprints for molecules representation and 7 classification algorithms with varying parameters. It appeared that the process of inactive set formation had a substantial impact on the machine learning methods performance.
  
  
  CONCLUSIONS
  The level of chemical space limitation determined the ability of tested classifiers to select potentially active molecules in virtual screening tasks, as for example DUDs (widely applied in docking experiments) did not provide proper selection of active molecules from databases with diverse structures. The study clearly showed that inactive compounds forming training set should be representative to the highest possible extent for libraries that undergo screening.
authors.affiliation: Department of Medicinal Chemistry, Institute of Pharmacology, Polish Academy of Sciences, Smętna 12, Kraków, 31-343, Poland. bojarski@if-pan.krakow.pl.
authors.forename: Sabina
authors.initials: S
authors.lastname: Smusz
authors.forename: Rafał
authors.initials: R
authors.lastname: Kurczab
authors.forename: Andrzej J
authors.initials: AJ
authors.lastname: Bojarski
references.citation: J Comput Aided Mol Des. 2007 Jan-Mar;21(1-3):53-62
references.pmid: 17205373
references.citation: Nucleic Acids Res. 2012 Jan;40(Database issue):D400-12
references.pmid: 22140110
references.citation: J Chem Inf Comput Sci. 2003 Nov-Dec;43(6):1947-58
references.pmid: 14632445
references.citation: J Comput Aided Mol Des. 2008 Mar-Apr;22(3-4):239-55
references.pmid: 18253702
references.citation: J Chem Inf Model. 2005 Jan-Feb;45(1):177-82
references.pmid: 15667143
references.citation: J Mol Graph Model. 2006 Nov;25(3):313-23
references.pmid: 16497524
references.citation: Comb Chem High Throughput Screen. 2009 May;12(4):332-43
references.pmid: 19442063
references.citation: J Chem Inf Model. 2006 May-Jun;46(3):1098-106
references.pmid: 16711730
references.citation: J Chem Inf Model. 2006 Nov-Dec;46(6):2423-31
references.pmid: 17125184
references.citation: J Chem Inf Model. 2010 Feb 22;50(2):205-16
references.pmid: 20088575
references.citation: J Chem Inf Comput Sci. 2003 Mar-Apr;43(2):493-500
references.pmid: 12653513
references.citation: J Mol Model. 2011 Sep;17(9):2133-41
references.pmid: 20857153
references.citation: Comb Chem High Throughput Screen. 2009 Jun;12(5):453-68
references.pmid: 19519325
references.citation: Bioinformatics. 2008 Nov 1;24(21):2518-25
references.pmid: 18784118
references.citation: Comb Chem High Throughput Screen. 2009 Jun;12(5):484-9
references.pmid: 19519327
references.citation: Comb Chem High Throughput Screen. 2009 May;12(4):344-57
references.pmid: 19442064
references.citation: Mol Pharm. 2009 Nov-Dec;6(6):1920-6
references.pmid: 19813762
references.citation: J Mol Graph Model. 2008 Jun;26(8):1276-86
references.pmid: 18218332
references.citation: J Comput Chem. 2011 May;32(7):1466-74
references.pmid: 21425294
references.citation: J Med Chem. 2006 Nov 16;49(23):6789-801
references.pmid: 17154509
references.citation: J Chem Inf Model. 2007 Jan-Feb;47(1):219-27
references.pmid: 17238267
references.citation: J Chem Inf Model. 2009 Sep;49(9):2101-10
references.pmid: 19689138
references.citation: Org Biomol Chem. 2004 Nov 21;2(22):3256-66
references.pmid: 15534703