Pereira2020 - Discovering growth Inhibitors of Neisseria gonorrhoeae with Machine Learning

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