Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae.

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 t

SUBMITTER: Pereira JC 

PROVIDER: S-EPMC8274436 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature