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Pereira2020 - Discovering growth Inhibitors of Neisseria gonorrhoeae with Machine Learning

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. Model Type: Predictive machine learning model. Model Relevance: The model predicts the probability of activity for the inhibition of the Antimicrobial pathogen N. gonorrhoeae. Model Encoded by: Sarima Chiorlu (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos5cl7

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2405080003 | BioModels | 2024-05-08

REPOSITORIES: BioModels

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

Pereira Janaina Cruz JC Daher Samer S SS Zorn Kimberley M KM Sherwood Matthew M Russo Riccardo R Perryman Alexander L AL Wang Xin X Freundlich Madeleine J MJ Ekins Sean S Freundlich Joel S JS

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]

PMID: 32661900

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