Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosisElectronic supplementary information (ESI) available: Supplementary figures and methods. The Bayesian models created in Discovery Studio are available from the authors upon written request. See DOI: 10.1039/c0mb00104jContributions of co-authors: SE directed and performed the computational analyses and wrote all versions of the manuscript, TK assisted with medicinal chemistry expertise,data

• Main Authors: Ekins, Sean, Kaneko, Takushi, Lipinski, Christopher A, Bradford, Justin, Dole, Krishna, Spektor, Anna, Gregory, Kellan, Blondeau, David, Ernst, Sylvia, Yang, Jeremy, Goncharoff, Nicko, Hohman, Moses M, Bunin, Barry A
• Format: Journal Article
• Language: English
• Published: 01.11.2010
• ISSN: 1742-206X; 1742-2051
• DOI: 10.1039/c0mb00104j

There is an urgent need for new drugs against tuberculosis which annually claims 1.7-1.8 million lives. One approach to identify potential leads is to screen in vitro small molecules against Mycobacterium tuberculosis (Mtb). Until recently there was no central repository to collect information on compounds screened. Consequently, it has been difficult to analyze molecular properties of compounds that inhibit the growth of Mtb in vitro . We have collected data from publically available sources on over 300 000 small molecules deposited in the Collaborative Drug Discovery TB Database. A cheminformatics analysis on these compounds indicates that inhibitors of the growth of Mtb have statistically higher mean logP, rule of 5 alerts, while also having lower HBD count, atom count and lower PSA (ChemAxon descriptors), compared to compounds that are classed as inactive. Additionally, Bayesian models for selecting Mtb active compounds were evaluated with over 100 000 compounds and, they demonstrated 10 fold enrichment over random for the top ranked 600 compounds. This represents a promising approach for finding compounds active against Mtb in whole cells screened under the same in vitro conditions. Various sets of Mtb hit molecules were also examined by various filtering rules used widely in the pharmaceutical industry to identify compounds with potentially reactive moieties. We found differences between the number of compounds flagged by these rules in Mtb datasets, malaria hits, FDA approved drugs and antibiotics. Combining these approaches may enable selection of compounds with increased probability of inhibition of whole cell Mtb activity. This work describes various filters that were used to evaluate Tuberculosis screening datasets, malaria hits, FDA approved drugs and antibiotics.