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mycoCSM: Using Graph-Based Signatures to Identify Safe Potent Hits against Mycobacteria.
J Chem Inf Model. 2020 Jul 16 [Online ahead of print]JC

Abstract

Development of new potent, safe drugs to treat Mycobacteria has proven to be challenging, with limited hit rates of initial screens restricting subsequent development efforts. Despite significant efforts and the evolution of quantitative structure-activity relationship as well as machine learning-based models for computationally predicting molecule bioactivity, there is an unmet need for efficient and reliable methods for identifying biologically active compounds against Mycobacterium that are also safe for humans. Here we developed mycoCSM, a graph-based signature approach to rapidly identify compounds likely to be active against bacteria from the genus Mycobacterium, or against specific Mycobacteria species. mycoCSM was trained and validated on eight organism-specific and for the first time a general Mycobacteria data set, achieving correlation coefficients of up to 0.89 on cross-validation and 0.88 on independent blind tests, when predicting bioactivity in terms of minimum inhibitory concentration. In addition, we also developed a predictor to identify those compounds likely to penetrate in necrotic tuberculosis foci, which achieved a correlation coefficient of 0.75. Together with a built-in estimator of the maximum tolerated dose in humans, we believe this method will provide a valuable resource to enrich screening libraries with potent, safe molecules. To provide simple guidance in the selection of libraries with favorable anti-Mycobacteria properties, we made mycoCSM freely available online at http://biosig.unimelb.edu.au/myco_csm.

Authors+Show Affiliations

Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne 3004, VIC, Australia. Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, 30 Flemington Rd, Parkville 3052, VIC, Australia. School of Computing and Information Systems, University of Melbourne, Parkville 3052, VIC, Australia.Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne 3004, VIC, Australia. Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne, 30 Flemington Rd, Parkville 3052, VIC, Australia. Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge CB2 1GA, England.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32615035

Citation

Pires, Douglas E V., and David B. Ascher. "MycoCSM: Using Graph-Based Signatures to Identify Safe Potent Hits Against Mycobacteria." Journal of Chemical Information and Modeling, 2020.
Pires DEV, Ascher DB. MycoCSM: Using Graph-Based Signatures to Identify Safe Potent Hits against Mycobacteria. J Chem Inf Model. 2020.
Pires, D. E. V., & Ascher, D. B. (2020). MycoCSM: Using Graph-Based Signatures to Identify Safe Potent Hits against Mycobacteria. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.0c00362
Pires DEV, Ascher DB. MycoCSM: Using Graph-Based Signatures to Identify Safe Potent Hits Against Mycobacteria. J Chem Inf Model. 2020 Jul 16; PubMed PMID: 32615035.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - mycoCSM: Using Graph-Based Signatures to Identify Safe Potent Hits against Mycobacteria. AU - Pires,Douglas E V, AU - Ascher,David B, Y1 - 2020/07/16/ PY - 2020/7/3/pubmed PY - 2020/7/3/medline PY - 2020/7/3/entrez JF - Journal of chemical information and modeling JO - J Chem Inf Model N2 - Development of new potent, safe drugs to treat Mycobacteria has proven to be challenging, with limited hit rates of initial screens restricting subsequent development efforts. Despite significant efforts and the evolution of quantitative structure-activity relationship as well as machine learning-based models for computationally predicting molecule bioactivity, there is an unmet need for efficient and reliable methods for identifying biologically active compounds against Mycobacterium that are also safe for humans. Here we developed mycoCSM, a graph-based signature approach to rapidly identify compounds likely to be active against bacteria from the genus Mycobacterium, or against specific Mycobacteria species. mycoCSM was trained and validated on eight organism-specific and for the first time a general Mycobacteria data set, achieving correlation coefficients of up to 0.89 on cross-validation and 0.88 on independent blind tests, when predicting bioactivity in terms of minimum inhibitory concentration. In addition, we also developed a predictor to identify those compounds likely to penetrate in necrotic tuberculosis foci, which achieved a correlation coefficient of 0.75. Together with a built-in estimator of the maximum tolerated dose in humans, we believe this method will provide a valuable resource to enrich screening libraries with potent, safe molecules. To provide simple guidance in the selection of libraries with favorable anti-Mycobacteria properties, we made mycoCSM freely available online at http://biosig.unimelb.edu.au/myco_csm. SN - 1549-960X UR - https://www.unboundmedicine.com/medline/citation/32615035/mycoCSM:_using_graph-based_signatures_to_identify_safe_potent_hits_against_Mycobacteria L2 - https://doi.org/10.1021/acs.jcim.0c00362 DB - PRIME DP - Unbound Medicine ER -
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