Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae
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RESEARCH PAPER
Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae Janaina Cruz Pereira 1 & Samer S. Daher 1 & Kimberley M. Zorn 2 & Matthew Sherwood 1 & Riccardo Russo 3 & Alexander L. Perryman 1,4 & Xin Wang 1,5 & Madeleine J. Freundlich 6 & Sean Ekins 2,7 & Joel S. Freundlich 1,3
Received: 3 April 2020 / Accepted: 6 July 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
ABSTRACT Purpose To advance fundamental biological and translational research with the bacterium Neisseria gonorrhoeae through the prediction of novel small molecule growth inhibitors via naïve Bayesian modeling methodology. Methods Inspection and curation of data from the publicly available ChEMBL web site for small molecule growth inhibition data of the bacterium Neisseria gonorrhoeae resulted in a training set for the construction of machine learning models. A naïve Bayesian model for bacterial growth inhibition was utilized in a workflow to predict novel antibacterial agents against this bacterium of global health relevance from a commercial library of >105 drug-like small molecules. Follow-up
efforts involved empirical assessment of the predictions and validation of the hits. Results Specifically, two small molecules were found that exhibited promising activity profiles and represent novel chemotypes for agents against N. gonorrrhoeae. Conclusions This represents, to the best of our knowledge, the first machine learning approach to successfully predict novel growth inhibitors of this bacterium. To assist the chemical tool and drug discovery fields, we have made our curated training set available as part of the Supplementary Material and the Bayesian model is accessible via the web.
KEY WORDS Diversity . machine learning model . Naïve Bayesian classifier . Neisseria gonorrhoeae
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11095-020-02876-y) contains supplementary material, which is available to authorized users. * Joel S. Freundlich [email protected]
ABBREVIATIONS CK MBC MCC MIC MLM PAINS PCA ROC S STI t1/2 WHO
Cohen’s Kappa minimum bactericidal concentration Matthews Correlation Coefficient minimum inhibitory concentration mouse liver microsome Pan Assay INterference compoundS Principal Component Analysis Receiver Operating Characteristic solubility sexually transmitted infection half-life World Health Organization
1
Department of Pharmacology, Physiology, and Neuroscience, Rutgers University New Jersey Medical School, I-503 185 South Orange Avenue, Newark, NJ 07103, USA
2
Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
3
Division of Infectious Disease, Department of Medicine and the Ruy V. Lourenço Center for the Study of Emerging and Re-emerging Pathogens, Rutgers University New Jersey Medical School, I-503 185 South Orange Avenue, Newark, NJ 07103, USA
4
Present address: Repare Therapeutics,, 7210 Rue Frederick-Banting Suite 100, Montreal, QC H4S 2A1, Canada
5
Prese
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