ArticleJournal of cheminformatics2013
TB Mobile: a mobile app for anti-tuberculosis molecules with known targets.
Article in Journal of cheminformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 33 citations in OpenAlex.
- Machine Learning Models forMolecular pharmaceutics · 2022Article
- Mobile Health Apps for Improvement of Tuberculosis Treatment: Descriptive Review.JMIR mHealth and uHealth · 2020Review
- Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.Molecular pharmaceutics · 2018Article
- In Silico Designed Axl Receptor Blocking Drug Candidates Against Zika Virus Infection.ACS omega · 2018Article
- 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
- Smartphone Applications to Support Tuberculosis Prevention and Treatment: Review and Evaluation.JMIR mHealth and uHealth · 2016Review
- Making Transporter Models for Drug-Drug Interaction Prediction Mobile.Drug metabolism and disposition: the biological fate of chemicals · 2015Article
- Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets.Journal of chemical information and modeling · 2015Article
- App-etite for change.Journal of computer-aided molecular design · 2015Article
- A virtual screen discovers novel, fragment-sized inhibitors of Mycobacterium tuberculosis InhA.Journal of chemical information and modeling · 2015Article
- Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.PloS one · 2015Article
- Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery.PLoS neglected tropical diseases · 2015Article
- Machine learning models identify molecules active against the Ebola virusF1000Research · 2015Article
- Bigger data, collaborative tools and the future of predictive drug discovery.Journal of computer-aided molecular design · 2014Article
- Looking back to the future: predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosis.Journal of chemical information and modeling · 2014Article
- Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis.Tuberculosis (Edinburgh, Scotland) · 2014Article
- Computational models for neglected diseases: gaps and opportunities.Pharmaceutical research · 2014Article
- New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0.Journal of cheminformatics · 2014Article
- Fusing dual-event data sets for Mycobacterium tuberculosis machine learning models and their evaluation.Journal of chemical information and modeling · 2013Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAn increasing number of researchers are focused on strategies for developing inhibitors of Mycobacterium tuberculosis (Mtb) as tuberculosis (TB) drugs.
resultsIn order to learn from prior work we have collated information on molecules screened versus Mtb and their targets which has been made available in the Collaborative Drug Discovery (CDD) database. This dataset contains published data on target, essentiality, links to PubMed, TBDB, TBCyc (which provides a pathway-based visualization of the entire cellular biochemical network) and human homolog information. The development of mobile cheminformatics apps could lower the barrier to drug discovery and promote collaboration. Therefore we have used this set of over 700 molecules screened versus Mtb and their targets to create a free mobile app (TB Mobile) that displays molecule structures and links to the bioinformatics data. By input of a molecular structures and performing a similarity search within the app we can infer potential targets or search by targets to retrieve compounds known to be active.
conclusionsTB Mobile may assist researchers as part of their workflow in identifying potential targets for hits generated from phenotypic screening and in prioritizing them for further follow-up. The app is designed to lower the barriers to accessing this information, so that all researchers with an interest in combatting this deadly disease can use it freely to the benefit of their own efforts.
Identifiers
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.