caa a22 4500 445313722 CHVBK 20180317142639.0 cr unu---uuuuu 170323e20110801xx s 000 0 eng 10.1007/s11095-011-0413-x doi (NATIONALLICENCE)springer-10.1007/s11095-011-0413-x Validating New Tuberculosis Computational Models with Public Whole Cell Screening Aerobic Activity Datasets [Elektronische Daten] [Sean Ekins, Joel Freundlich] ABSTRACT: Purpose: 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. Springer Science+Business Media, LLC, 2011 Bayesian models nationallicence Collaborative Drug Discovery nationallicence function class fingerprints nationallicence mycobacterium tuberculosis nationallicence Quantitative Structure Activity Relationship nationallicence tuberculosis nationallicence tuberculosis database nationallicence Ekins Sean Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, 94010, Burlingame, California, USA aut Freundlich Joel Department of Biochemistry and Biophysics, Texas A&M University, 77843, College Station, Texas, USA aut Pharmaceutical Research Springer US; http://www.springer-ny.com 28/8(2011-08-01), 1859-1869 0724-8741 28:8<1859 2011 28 11095 https://doi.org/10.1007/s11095-011-0413-x text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11095-011-0413-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ekins Sean Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, 94010, Burlingame, California, USA aut NATIONALLICENCE 700 1- Freundlich Joel Department of Biochemistry and Biophysics, Texas A&M University, 77843, College Station, Texas, USA aut NATIONALLICENCE 773 0- Pharmaceutical Research Springer US; http://www.springer-ny.com 28/8(2011-08-01), 1859-1869 0724-8741 28:8<1859 2011 28 11095 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer