title: Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets.
abstract: BACKGROUND
  Tuberculosis is a contagious disease caused by Mycobacterium tuberculosis (Mtb), affecting more than two billion people around the globe and is one of the major causes of morbidity and mortality in the developing world. Recent reports suggest that Mtb has been developing resistance to the widely used anti-tubercular drugs resulting in the emergence and spread of multi drug-resistant (MDR) and extensively drug-resistant (XDR) strains throughout the world. In view of this global epidemic, there is an urgent need to facilitate fast and efficient lead identification methodologies. Target based screening of large compound libraries has been widely used as a fast and efficient approach for lead identification, but is restricted by the knowledge about the target structure. Whole organism screens on the other hand are target-agnostic and have been now widely employed as an alternative for lead identification but they are limited by the time and cost involved in running the screens for large compound libraries. This could be possibly be circumvented by using computational approaches to prioritize molecules for screening programmes.
  
  
  RESULTS
  We utilized physicochemical properties of compounds to train four supervised classifiers (Naïve Bayes, Random Forest, J48 and SMO) on three publicly available bioassay screens of Mtb inhibitors and validated the robustness of the predictive models using various statistical measures.
  
  
  CONCLUSIONS
  This study is a comprehensive analysis of high-throughput bioassay data for anti-tubercular activity and the application of machine learning approaches to create target-agnostic predictive models for anti-tubercular agents.
authors.affiliation: GN Ramachandran Knowledge Center for Genome Informatics, CSIR Institute of Genomics and Integrative Biology (CSIR-IGIB), Mall Road, Delhi - 110007, India. jaleel.uc@gmail.com.
authors.forename: Vinita
authors.initials: V
authors.lastname: Periwal
authors.forename: Jinuraj K
authors.initials: JK
authors.lastname: Rajappan
authors.forename: Abdul Uc
authors.initials: AU
authors.lastname: Jaleel
authors.forename: Vinod
authors.initials: V
authors.lastname: Scaria
references.citation: Mol Biosyst. 2010 Nov;6(11):2316-2324
references.pmid: 20835433
references.citation: Comb Chem High Throughput Screen. 2008 Sep;11(8):677-85
references.pmid: 18795887
references.citation: Tuberculosis (Edinb). 2009 Sep;89(5):354-63
references.pmid: 19783214
references.citation: Drug Metab Dispos. 2009 Mar;37(3):658-64
references.pmid: 19056915
references.citation: J Chem Inf Model. 2005 Mar-Apr;45(2):515-22
references.pmid: 15807517
references.citation: Nucleic Acids Res. 2009 Jul;37(Web Server issue):W623-33
references.pmid: 19498078
references.citation: Proc Natl Acad Sci U S A. 1994 Mar 29;91(7):2428-9
references.pmid: 8146134
references.citation: Comb Chem High Throughput Screen. 2009 May;12(4):332-43
references.pmid: 19442063
references.citation: Tuberculosis (Edinb). 2009 Sep;89(5):334-53
references.pmid: 19758845
references.citation: Bioinformatics. 2007 Oct 1;23(19):2507-17
references.pmid: 17720704
references.citation: Pharm Res. 2011 Aug;28(8):1859-69
references.pmid: 21547522
references.citation: Mol Pharm. 2010 Oct 4;7(5):1708-14
references.pmid: 20799726
references.citation: Infect Disord Drug Targets. 2007 Jun;7(2):92-104
references.pmid: 17970221
references.citation: Mol Biosyst. 2010 May;6(5):840-51
references.pmid: 20567770
references.citation: Drug Discov Today. 1999 Oct;4(10):447-448
references.pmid: 10481138
references.citation: Curr Top Med Chem. 2008;8(18):1691-709
references.pmid: 19075775
references.citation: J Cheminform. 2009 Dec 22;1:21
references.pmid: 20150999
references.citation: Trends Microbiol. 2011 Feb;19(2):65-74
references.pmid: 21129975
references.citation: Bioinformatics. 2010 Aug 1;26(15):1841-8
references.pmid: 20529890