Dataset Information
Pereira2020 - Discovering growth Inhibitors of Neisseria gonorrhoeae with Machine Learning
Ontology highlight
ABSTRACT: The authors curated a dataset of 282 compounds from ChEMBL, of which 160 (56.7%) were labeled as active N. gonorrhoeae inhibitor compounds. They used this dataset to build a naïve Bayesian model and used it to screen a commercial library. With this method,they identified and validated two hits compound. Implementation of this model code by Ersilia is available here:
https://github.com/ersilia-os/eos5cl7
ORGANISM(S): Neisseria gonorrhoeae
SUBMITTER: Zainab Ashimiyu-Abdusalam
PROVIDER: MODEL2405080003 | biostudies-other |
SECONDARY ACCESSION(S): 32661900
REPOSITORIES: biostudies-other
Publications
Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae.
Pharmaceutical research 20200713 7
<h4>Purpose</h4>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.<h4>Methods</h4>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 b ...[more]
