ArticleJournal of cheminformatics2014
New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0.
Article in Journal of cheminformatics, 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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Who cites it
23 citing papers in PubMed.
- Machine Learning Models forMolecular pharmaceutics · 2022Article
- Bayesian Modeling and Intrabacterial Drug Metabolism Applied to Drug-ResistantACS infectious diseases · 2021Article
- Repurposing Approved Drugs as Inhibitors of KPharmaceutical research · 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
- The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching.Journal of cheminformatics · 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
- The polypharmacology browser: a web-based multi-fingerprint target prediction tool using ChEMBL bioactivity data.Journal of cheminformatics · 2017Article
- The Next Era: Deep Learning in Pharmaceutical Research.Pharmaceutical research · 2016Review
- 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
- Smartphone Applications to Support Tuberculosis Prevention and Treatment: Review and Evaluation.JMIR mHealth and uHealth · 2016Review
- Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.Pharmaceutical research · 2016Article
- 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
- 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
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3 authors.
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Abstract
backgroundWe recently developed a freely available mobile app (TB Mobile) for both iOS and Android platforms that displays Mycobacterium tuberculosis (Mtb) active molecule structures and their targets with links to associated data. The app was developed to make target information available to as large an audience as possible.
resultsWe now report a major update of the iOS version of the app. This includes enhancements that use an implementation of ECFP_6 fingerprints that we have made open source. Using these fingerprints, the user can propose compounds with possible anti-TB activity, and view the compounds within a cluster landscape. Proposed compounds can also be compared to existing target data, using a näive Bayesian scoring system to rank probable targets. We have curated an additional 60 new compounds and their targets for Mtb and added these to the original set of 745 compounds. We have also curated 20 further compounds (many without targets in TB Mobile) to evaluate this version of the app with 805 compounds and associated targets.
conclusionsTB Mobile can now manage a small collection of compounds that can be imported from external sources, or exported by various means such as email or app-to-app inter-process communication. This means that TB Mobile can be used as a node within a growing ecosystem of mobile apps for cheminformatics. It can also cluster compounds and use internal algorithms to help identify potential targets based on molecular similarity. TB Mobile represents a valuable dataset, data-visualization aid and target prediction tool.
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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.