ArticleJournal of chemical information and modeling2014
Are bigger data sets better for machine learning? Fusing single-point and dual-event dose response data for 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 24 papers.
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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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Who cites it
24 citing papers in PubMed, 41 citations in OpenAlex.
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- High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents.Journal of medicinal chemistry · 2023Article
- Machine Learning Models forMolecular pharmaceutics · 2022Article
- Mycobacterium abscessus drug discovery using machine learning.Tuberculosis (Edinburgh, Scotland) · 2022Article
- Quantum Machine Learning Algorithms for Drug Discovery Applications.Journal of chemical information and modeling · 2021Article
- Bioactivity Comparison across Multiple Machine Learning Algorithms Using over 5000 Datasets for Drug Discovery.Molecular pharmaceutics · 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
- Cinnamic Derivatives as Antitubercular Agents: Characterization by Quantitative Structure-Activity Relationship Studies.Molecules (Basel, Switzerland) · 2020Article
- Intrabacterial Metabolism Obscures the Successful Prediction of an InhA Inhibitor ofACS infectious diseases · 2019Article
- Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.Molecular pharmaceutics · 2018Article
- Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.Molecular pharmaceutics · 2017Article
- Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB).Drug discovery today · 2017Review
- 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
- Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses.Journal of chemical information and modeling · 2016Article
- Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data.Pharmaceutical research · 2016Article
- Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets.Journal of chemical information and modeling · 2015Article
- Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.PloS one · 2015Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 1 country.
Funding
Abstract
Tuberculosis is a major, neglected disease for which the quest to find new treatments continues. There is an abundance of data from large phenotypic screens in the public domain against Mycobacterium tuberculosis (Mtb). Since machine learning methods can learn from past data, we were interested in addressing whether more data builds better models. We now describe using Bayesian machine learning to assess whether we can improve our models by combining the large quantities of single-point data with the much smaller (higher quality) dual-event data sets, which use both dose-response data for both whole-cell antitubercular activity and Vero cell cytotoxicity. We have evaluated 12 models ranging from different single-point, dual-event dose-response, single-point and dual-event dose-response as well as combined data sets for three distinct data sets from the same laboratory. We used a fourth data set of active and inactive compounds from the same group as well as a smaller set of 177 active compounds from GlaxoSmithKline as test sets. Our data suggest combining single-point with dual-event dose-response data does not diminish the internal or external predictive ability of the models based on the receiver operator curve (ROC) for these models (internal ROC range 0.83-0.91, external ROC range 0.62-0.83) compared to the orders of magnitude smaller dual-event models (internal ROC range 0.6-0.83 and external ROC 0.54-0.83). In conclusion, models developed with 1200-5000 compounds appear to be as predictive as those generated with 25 000-350 000 molecules. Our results have implications for justifying further high-throughput screening versus focused testing based on model predictions.
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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.