ArticleJournal of chemical information and modeling2014
Looking back to the future: predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosis.
Article in Journal of chemical information and modeling, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 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
- Development of machine learning models to identify potentially active compounds against tuberculosis.Scientific reports · 2025Article
- Synthesis and screening of cyclic diketone indanedione derivatives as future scaffolds for neutrophil elastase inhibition.RSC advances · 2023Article
- Graph-Based Feature Selection Approach for Molecular Activity Prediction.Journal of chemical information and modeling · 2022Article
- Pre-Clinical Tools for Predicting Drug Efficacy in Treatment of Tuberculosis.Microorganisms · 2022Review
- Machine Learning Models forMolecular pharmaceutics · 2022Article
- Mycobacterium abscessus drug discovery using machine learning.Tuberculosis (Edinburgh, Scotland) · 2022Article
- Molecular modeling of a series of dehydroquinate dehydratase type II inhibitors of Mycobacterium tuberculosis and design of new binders.Molecular diversity · 2021Article
- Structure-basedComputational and structural biotechnology journal · 2021Review
- 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
- Leveraging Computational Modeling to Understand Infectious Diseases.Current pathobiology reports · 2020Review
- Article
- Underscoring interstrain variability and the impact of growth conditions on associated antimicrobial susceptibilities in preclinical testing of novel antimicrobial drugs.Critical reviews in microbiology · 2019Review
- Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.Molecular pharmaceutics · 2018Article
- Novel Pyrimidines as Antitubercular Agents.Antimicrobial agents and chemotherapy · 2018Article
- Data Mining and Computational Modeling of High-Throughput Screening Datasets.Methods in molecular biology (Clifton, N.J.) · 2018Article
- A High-throughput Compatible Assay to Evaluate Drug Efficacy against Macrophage Passaged Mycobacterium tuberculosis.Journal of visualized experiments : JoVE · 2017Article
- Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB).Drug discovery today · 2017Review
- Learning from the past for TB drug discovery in the future.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.
Funding
Abstract
Selecting and translating in vitro leads for a disease into molecules with in vivo activity in an animal model of the disease is a challenge that takes considerable time and money. As an example, recent years have seen whole-cell phenotypic screens of millions of compounds yielding over 1500 inhibitors of Mycobacterium tuberculosis (Mtb). These must be prioritized for testing in the mouse in vivo assay for Mtb infection, a validated model utilized to select compounds for further testing. We demonstrate learning from in vivo active and inactive compounds using machine learning classification models (Bayesian, support vector machines, and recursive partitioning) consisting of 773 compounds. The Bayesian model predicted 8 out of 11 additional in vivo actives not included in the model as an external test set. Curation of 70 years of Mtb data can therefore provide statistically robust computational models to focus resources on in vivo active small molecule antituberculars. This highlights a cost-effective predictor for in vivo testing elsewhere in other diseases.
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What OpenQuestion holds
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.