ArticlePloS one2013
Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
What it found
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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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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
42 citing papers in PubMed, 55 citations in OpenAlex.
- Global Research Trends and Insights on AI Usage in Tuberculosis: Bibliometric Analysis.JMIR medical informatics · 2026Article
- Integrating computational and biological approaches for the discovery of putative MmpL3 inhibitors against Mycobacterium tuberculosis.Molecular diversity · 2026Article
- Recent Applications of Artificial Intelligence in Discovery of New Antibacterial Agents.Advances and applications in bioinformatics and chemistry : AABC · 2024Review
- High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents.Journal of medicinal chemistry · 2023Article
- 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
- Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases.Frontiers in chemistry · 2021Review
- Molecule Property Analyses of Active Compounds forJournal of medicinal chemistry · 2020Review
- Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae.Pharmaceutical research · 2020Article
- Intrabacterial Metabolism Obscures the Successful Prediction of an InhA Inhibitor ofACS infectious diseases · 2019Article
- High-throughput screening and Bayesian machine learning for copper-dependent inhibitors of Staphylococcus aureus.Metallomics : integrated biometal science · 2019Article
- Evaluation and Identification of the Neuroprotective Compounds of Xiaoxuming Decoction by Machine Learning: A Novel Mode to Explore the Combination Rules in Traditional Chinese Medicine Prescription.BioMed research international · 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
- An RNAi-based high-throughput screening assay to identify small molecule inhibitors of hepatitis B virus replication.The Journal of biological chemistry · 2017Article
- Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB).Drug discovery today · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
High-throughput screening (HTS) in whole cells is widely pursued to find compounds active against Mycobacterium tuberculosis (Mtb) for further development towards new tuberculosis (TB) drugs. Hit rates from these screens, usually conducted at 10 to 25 µM concentrations, typically range from less than 1% to the low single digits. New approaches to increase the efficiency of hit identification are urgently needed to learn from past screening data. The pharmaceutical industry has for many years taken advantage of computational approaches to optimize compound libraries for in vitro testing, a practice not fully embraced by academic laboratories in the search for new TB drugs. Adapting these proven approaches, we have recently built and validated Bayesian machine learning models for predicting compounds with activity against Mtb based on publicly available large-scale HTS data from the Tuberculosis Antimicrobial Acquisition Coordinating Facility. We now demonstrate the largest prospective validation to date in which we computationally screened 82,403 molecules with these Bayesian models, assayed a total of 550 molecules in vitro, and identified 124 actives against Mtb. Individual hit rates for the different datasets varied from 15-28%. We have identified several FDA approved and late stage clinical candidate kinase inhibitors with activity against Mtb which may represent starting points for further optimization. The computational models developed herein and the commercially available molecules derived from them are now available to any group pursuing Mtb drug discovery.
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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.