ArticlePharmaceutical research2012
Combining cheminformatics methods and pathway analysis to identify molecules with whole-cell activity against Mycobacterium tuberculosis.
Article in Pharmaceutical research, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
20 citing papers in PubMed.
- Machine Learning Models forMolecular pharmaceutics · 2022Article
- The Chemical Property Position of Bedaquiline Construed by a Chemical Global Positioning System-Natural Product.Molecules (Basel, Switzerland) · 2022Article
- Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.Molecular pharmaceutics · 2018Article
- Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB).Drug discovery today · 2017Review
- Article
- 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
- Evolution of a thienopyrimidine antitubercular relying on medicinal chemistry and metabolomics insights.Tetrahedron letters · 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
- 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
- Combining computational methods for hit to lead optimization in Mycobacterium tuberculosis drug discovery.Pharmaceutical research · 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
- Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery.Chemistry & biology · 2013Article
- TB Mobile: a mobile app for anti-tuberculosis molecules with known targets.Journal of cheminformatics · 2013Article
- Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models.PloS one · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
purposeNew strategies for developing inhibitors of Mycobacterium tuberculosis (Mtb) are required in order to identify the next generation of tuberculosis (TB) drugs. Our approach leverages the integration of intensive data mining and curation and computational approaches, including cheminformatics combined with bioinformatics, to suggest biological targets and their small molecule modulators.
methodsWe now describe an approach that uses the TBCyc pathway and genome database, the Collaborative Drug Discovery database of molecules with activity against Mtb and their associated targets, a 3D pharmacophore approach and Bayesian models of TB activity in order to select pathways and metabolites and ultimately prioritize molecules that may be acting as substrate mimics and exhibit activity against TB.
resultsIn this study we combined the TB cheminformatics and pathways databases that enabled us to computationally search >80,000 vendor available molecules and ultimately test 23 compounds in vitro that resulted in two compounds (N-(2-furylmethyl)-N'-[(5-nitro-3-thienyl)carbonyl]thiourea and N-[(5-nitro-3-thienyl)carbonyl]-N'-(2-thienylmethyl)thiourea) proposed as mimics of D-fructose 1,6 bisphosphate, (MIC of 20 and 40 μg/ml, respectively).
conclusionThis is a simple yet novel approach that has the potential to identify inhibitors of bacterial growth as illustrated by compounds identified in this study that have activity against Mtb.
Indexed as
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.