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Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis.
Tuberculosis (Edinb). 2014 Mar; 94(2):162-9.T

Abstract

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.

Authors+Show Affiliations

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.Infectious Disease Research Institute, Seattle, WA, USA.Infectious Disease Research Institute, Seattle, WA, USA.Infectious Disease Research Institute, Seattle, WA, USA.Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010, USA.

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

24440548

Citation

Ekins, Sean, et al. "Bayesian Models for Screening and TB Mobile for Target Inference With Mycobacterium Tuberculosis." Tuberculosis (Edinburgh, Scotland), vol. 94, no. 2, 2014, pp. 162-9.
Ekins S, Casey AC, Roberts D, et al. Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis. Tuberculosis (Edinb). 2014;94(2):162-9.
Ekins, S., Casey, A. C., Roberts, D., Parish, T., & Bunin, B. A. (2014). Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis. Tuberculosis (Edinburgh, Scotland), 94(2), 162-9. https://doi.org/10.1016/j.tube.2013.12.001
Ekins S, et al. Bayesian Models for Screening and TB Mobile for Target Inference With Mycobacterium Tuberculosis. Tuberculosis (Edinb). 2014;94(2):162-9. PubMed PMID: 24440548.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis. AU - Ekins,Sean, AU - Casey,Allen C, AU - Roberts,David, AU - Parish,Tanya, AU - Bunin,Barry A, Y1 - 2013/12/19/ PY - 2013/08/21/received PY - 2013/12/04/revised PY - 2013/12/09/accepted PY - 2014/1/21/entrez PY - 2014/1/21/pubmed PY - 2014/12/15/medline KW - Bayesian models KW - Collaborative drug discovery tuberculosis database KW - Function class fingerprints KW - Mycobacterium tuberculosis KW - Virtual screening SP - 162 EP - 9 JF - Tuberculosis (Edinburgh, Scotland) JO - Tuberculosis (Edinb) VL - 94 IS - 2 N2 - 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. SN - 1873-281X UR - https://www.unboundmedicine.com/medline/citation/24440548/Bayesian_models_for_screening_and_TB_Mobile_for_target_inference_with_Mycobacterium_tuberculosis_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1472-9792(13)00200-X DB - PRIME DP - Unbound Medicine ER -