ArticlePharmaceutical research2020
Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae.
Article in Pharmaceutical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed, 9 citations in OpenAlex.
- Artificial Intelligence-Guided Phenotypic Drug Repurposing Against Streptococcus pneumoniae.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Deep learning-enabled discovery of antibiotics effective againstScience translational medicine · 2026Article
- Identification of antigonorrhoeal phytochemical lead compounds using a metabolomics-guided approach.Folia microbiologica · 2026Article
- Article
- A comprehensive review for machine learning based human papillomavirus detection in forensic identification with multiple medical samples.Frontiers in microbiology · 2023Review
- Random Forest Model Predictions Afford Dual-Stage Antimalarial Agents.ACS infectious diseases · 2022Article
- Article
- Mycobacterium abscessus drug discovery using machine learning.Tuberculosis (Edinburgh, Scotland) · 2022Article
- Bayesian Modeling and Intrabacterial Drug Metabolism Applied to Drug-ResistantACS infectious diseases · 2021Article
- Discovery of 5-Nitro-6-thiocyanatopyrimidines as Inhibitors ofACS medicinal chemistry letters · 2021Article
- Random Forest Model Prediction of Compound Oral Exposure in the Mouse.ACS pharmacology & translational science · 2021Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 4 countries.
Funding
Abstract
purposeTo 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.
methodsInspection 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 >10
resultsSpecifically, two small molecules were found that exhibited promising activity profiles and represent novel chemotypes for agents against N. gonorrrhoeae.
conclusionsThis 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. Graphical Abstract.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.