Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis.
The search for compounds active against Mycobacterium tuberculosis is reliant upon high-throughput screening (HTS) in whole cells. We have used Bayesian machine learning models which can predict anti-tubercular activity to filter an internal library of over 150,000 compounds prior to in vitro testing. We used this to select and test 48 compounds in vitro; 11 were active with MIC values ranging from 0.4 μM to 10.2 μM, giving a high hit rate of 22.9%. Among the hits, we identified several compounds belonging to the same series including five quinolones (including ciprofloxacin), three molecules with long aliphatic linkers and three singletons. This approach represents a rapid method to prioritize compounds for testing that can be used alongside medicinal chemistry insight and other filters to identify active molecules. Such models can significantly increase the hit rate of HTS, above the usual 1% or lower rates seen. In addition, the potential targets for the 11 molecules were predicted using TB Mobile and clustering alongside a set of over 740 molecules with known M. tuberculosis target annotations. These predictions may serve as a mechanism for prioritizing compounds for further optimization.
Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA; Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526, USA. Electronic address: ekinssean@yahoo.com.
Sean
S
Ekins
Infectious Disease Research Institute, Seattle, WA, USA.
Allen C
AC
Casey
Infectious Disease Research Institute, Seattle, WA, USA.
David
D
Roberts
Infectious Disease Research Institute, Seattle, WA, USA.
Tanya
T
Parish
Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA.
Barry A
BA
Bunin
D000995:Antitubercular Agents / Q000737:chemistry / Q000494:pharmacology*; D001499:Bayes Theorem; D030562:Databases, Protein; D055808:Drug Discovery; D005260:Female; D006801:Humans; D008297:Male; D009169:Mycobacterium tuberculosis / Q000187:drug effects*; D015203:Reproducibility of Results; D054852:Small Molecule Libraries / Q000494:pharmacology*
D000995:Antitubercular Agents; D054852:Small Molecule Libraries
Bayesian models; Collaborative drug discovery tuberculosis database; Function class fingerprints; Mycobacterium tuberculosis; Virtual screening