title: Validating new tuberculosis computational models with public whole cell screening aerobic activity datasets.
abstract: OBJECTIVE
  The search for small molecules with activity against Mycobacterium tuberculosis (Mtb) increasingly uses high throughput screening and computational methods. Several public datasets from the Collaborative Drug Discovery Tuberculosis (CDD TB) database have been evaluated with cheminformatics approaches to validate their utility and suggest compounds for testing.
  
  
  METHODS
  Previously reported Bayesian classification models were used to predict a set of 283 Novartis compounds tested against Mtb (containing aerobic and anaerobic hits) and to search FDA approved drugs. The Novartis compounds were also filtered with computational SMARTS alerts to identify potentially undesirable substructures.
  
  
  RESULTS
  Using the Novartis compounds as a test set for the Bayesian models demonstrated a >4.0-fold enrichment over random screening for finding aerobic hits not in the computational models (N = 34). A 10-fold enrichment was observed for finding Mtb active compounds in the FDA drugs database. 85.9% of the Novartis compounds failed the Abbott SMARTS alerts, a value substantially higher than for known TB drugs. Higher levels of failures of SMARTS filters from different groups also correlate with the number of Lipinski violations.
  
  
  CONCLUSIONS
  These computational approaches may assist in finding desirable leads for Tuberculosis drug discovery.
authors.affiliation: Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, USA. sekins@collaborativedrug.com
authors.forename: Sean
authors.initials: S
authors.lastname: Ekins
authors.forename: Joel S
authors.initials: JS
authors.lastname: Freundlich
mesh_terms: D000995:Antitubercular Agents / Q000737:chemistry / Q000494:pharmacology*; D001499:Bayes Theorem; D003198:Computer Simulation; D016208:Databases, Factual; D055808:Drug Discovery / Q000379:methods; D008954:Models, Biological; D009169:Mycobacterium tuberculosis / Q000187:drug effects; D054852:Small Molecule Libraries / Q000737:chemistry / Q000494:pharmacology*; D014376:Tuberculosis / Q000188:drug therapy*
chemical_list: D000995:Antitubercular Agents; D054852:Small Molecule Libraries