ArticleTuberculosis (Edinburgh, Scotland)2014
Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis.
Article in Tuberculosis (Edinburgh, Scotland), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed.
- Machine Learning in Tuberculosis Research: A Global Bibliometric Analysis of Diagnostic, Prognostic, and Drug Discovery Trends.Therapeutic innovation & regulatory science · 2026Article
- Advances in computational frameworks in the fight against TB: The way forward.Frontiers in pharmacology · 2023Review
- Machine Learning Models forMolecular pharmaceutics · 2022Article
- Mycobacterium abscessus drug discovery using machine learning.Tuberculosis (Edinburgh, Scotland) · 2022Article
- Recent advances in drug repurposing using machine learning.Current opinion in chemical biology · 2021Review
- A Machine Learning Strategy for Drug Discovery Identifies Anti-Schistosomal Small Molecules.ACS infectious diseases · 2021Article
- Molecule Property Analyses of Active Compounds forJournal of medicinal chemistry · 2020Review
- High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureus.Metallomics : integrated biometal science · 2019Article
- Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.Molecular pharmaceutics · 2018Article
- Data Mining and Computational Modeling of High-Throughput Screening Datasets.Methods in molecular biology (Clifton, N.J.) · 2018Article
- Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.Molecular pharmaceutics · 2017Article
- Learning from the past for TB drug discovery in the future.Drug discovery today · 2017Review
- Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB).Drug discovery today · 2017Review
- Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014-2015).Journal of chemical information and modeling · 2016Article
- Predictive modeling targets thymidylate synthase ThyX in Mycobacterium tuberculosis.Scientific reports · 2016Article
- Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses.Journal of chemical information and modeling · 2016Article
- Machine learning models identify molecules active against the Ebola virusF1000Research · 2015Article
- Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery.PLoS neglected tropical diseases · 2015Article
- Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.PloS one · 2015Article
- Bigger data, collaborative tools and the future of predictive drug discovery.Journal of computer-aided molecular design · 2014Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The search for compounds active against Mycobacterium tuberculosis is reliant upon high-throughput screening (HTS) in whole cells. We have used Bayesian machine learning models which can predict anti-tubercular activity to filter an internal library of over 150,000 compounds prior to in vitro testing. We used this to select and test 48 compounds in vitro; 11 were active with MIC values ranging from 0.4 μM to 10.2 μM, giving a high hit rate of 22.9%. Among the hits, we identified several compounds belonging to the same series including five quinolones (including ciprofloxacin), three molecules with long aliphatic linkers and three singletons. This approach represents a rapid method to prioritize compounds for testing that can be used alongside medicinal chemistry insight and other filters to identify active molecules. Such models can significantly increase the hit rate of HTS, above the usual 1% or lower rates seen. In addition, the potential targets for the 11 molecules were predicted using TB Mobile and clustering alongside a set of over 740 molecules with known M. tuberculosis target annotations. These predictions may serve as a mechanism for prioritizing compounds for further optimization.
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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.