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Are Bigger Data Sets Better for Machine Learning? Fusing Single-Point and Dual-Event Dose Response Data for Mycobacterium tuberculosis

https://doi.org/10.1021/ci500264r
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/cmdc.200700026openalex-snapshot
Analysis of Pharmacology Data and the Prediction of Adverse Drug Reactions and Off‐Target Effects from Chemical Structure
inferredcites →https://doi.org/10.1002/cmdc.201200428openalex-snapshot
Fueling Open‐Source Drug Discovery: 177 Small‐Molecule Leads against Tuberculosis
inferredcites →https://doi.org/10.1002/minf.201300076openalex-snapshot
Using Graph Indices for the Analysis and Comparison of Chemical Datasets
inferredcites →https://doi.org/10.1007/978-1-62703-342-8_16openalex-snapshot
Computational Models for Tuberculosis Drug Discovery
inferredcites →https://doi.org/10.1007/s11030-006-9041-5openalex-snapshot
Cheminformatics analysis and learning in a data pipelining environment
inferredcites →https://doi.org/10.1007/s11095-010-0229-0openalex-snapshot
Chemical Space: Missing Pieces in Cheminformatics
inferredcites →https://doi.org/10.1007/s11095-011-0413-xopenalex-snapshot
Validating New Tuberculosis Computational Models with Public Whole Cell Screening Aerobic Activity Datasets
inferredcites →https://doi.org/10.1007/s11095-013-1170-9openalex-snapshot
Computational Models for Neglected Diseases: Gaps and Opportunities
inferredcites →https://doi.org/10.1007/s11095-013-1172-7openalex-snapshot
Combining Computational Methods for Hit to Lead Optimization in Mycobacterium Tuberculosis Drug Discovery
inferredcites →https://doi.org/10.1016/j.bmcl.2013.07.006openalex-snapshot
A medicinal chemists’ guide to the unique difficulties of lead optimization for tuberculosis
inferredcites →https://doi.org/10.1016/j.chembiol.2013.01.011openalex-snapshot
Bayesian Models Leveraging Bioactivity and Cytotoxicity Information for Drug Discovery
inferredcites →https://doi.org/10.1016/j.drudis.2008.11.015openalex-snapshot
Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery
inferredcites →https://doi.org/10.1016/j.drudis.2010.08.010openalex-snapshot
When pharmaceutical companies publish large datasets: an abundance of riches or fool's gold?
inferredcites →https://doi.org/10.1016/j.drudis.2011.07.007openalex-snapshot
A quality alert and call for improved curation of public chemistry databases
inferredcites →https://doi.org/10.1016/j.drudis.2012.02.013openalex-snapshot
Towards a gold standard: regarding quality in public domain chemistry databases and approaches to improving the situation
inferredcites →https://doi.org/10.1016/j.drudis.2012.11.005openalex-snapshot
Challenges and recommendations for obtaining chemical structures of industry-provided repurposing candidates
inferredcites →https://doi.org/10.1016/j.drudis.2013.07.001openalex-snapshot
Phenotypic screens as a renewed approach for drug discovery
inferredcites →https://doi.org/10.1016/j.ejmech.2013.10.029openalex-snapshot
Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds
inferredcites →https://doi.org/10.1016/j.tim.2010.10.005openalex-snapshot
Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery
inferredcites →https://doi.org/10.1016/j.tube.2009.05.008openalex-snapshot
High-throughput screening for inhibitors of Mycobacterium tuberculosis H37Rv
inferredcites →https://doi.org/10.1016/j.tube.2009.07.006openalex-snapshot
Antituberculosis activity of the molecular libraries screening center network library
inferredcites →https://doi.org/10.1016/j.tube.2011.05.005openalex-snapshot
High throughput screening of a library based on kinase inhibitor scaffolds against Mycobacterium tuberculosis H37Rv
inferredcites →https://doi.org/10.1016/j.tube.2012.07.003openalex-snapshot
Comprehensive analysis of methods used for the evaluation of compounds against Mycobacterium tuberculosis
inferredcites →https://doi.org/10.1016/j.tube.2013.09.003openalex-snapshot
Why are membrane targets discovered by phenotypic screens and genome sequencing in Mycobacterium tuberculosis?
inferredcites →https://doi.org/10.1016/j.tube.2013.12.001openalex-snapshot
Bayesian models for screening and TB Mobile for target inference with Mycobacterium tuberculosis
inferredcites →https://doi.org/10.1016/s0006-2952(99)00253-1openalex-snapshot
Use of genomics and combinatorial chemistry in the development of new antimycobacterial drugs
inferredcites →https://doi.org/10.1016/s1473-3099(13)70328-1openalex-snapshot
New antituberculosis drugs, regimens, and adjunct therapies: needs, advances, and future prospects
inferredcites →https://doi.org/10.1021/cc020071popenalex-snapshot
Combinatorial Lead Optimization of [1,2]-Diamines Based on Ethambutol as Potential Antituberculosis Preclinical Candidates
inferredcites →https://doi.org/10.1021/ci0601315openalex-snapshot
Improved Naïve Bayesian Modeling of Numerical Data for Absorption, Distribution, Metabolism and Excretion (ADME) Property Prediction
inferredcites →https://doi.org/10.1021/ci300421nopenalex-snapshot
Discovery of Novel Antimalarial Compounds Enabled by QSAR-Based Virtual Screening
inferredcites →https://doi.org/10.1021/ci400480sopenalex-snapshot
Fusing Dual-Event Data Sets for Mycobacterium tuberculosis Machine Learning Models and Their Evaluation
inferredcites →https://doi.org/10.1021/ci500077vopenalex-snapshot
Looking Back to the Future: Predicting in Vivo Efficacy of Small Molecules versus Mycobacterium tuberculosis
inferredcites →https://doi.org/10.1021/ci800143nopenalex-snapshot
Global Bayesian Models for the Prioritization of Antitubercular Agents
inferredcites →https://doi.org/10.1021/jm9602928openalex-snapshot
The Properties of Known Drugs. 1. Molecular Frameworks
inferredcites →https://doi.org/10.1038/31159openalex-snapshot
Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence
inferredcites →https://doi.org/10.1038/nature09099openalex-snapshot
Chemical genetics of Plasmodium falciparum
inferredcites →https://doi.org/10.1038/nature09107openalex-snapshot
Thousands of chemical starting points for antimalarial lead identification
inferredcites →https://doi.org/10.1038/nature09657openalex-snapshot
The challenge of new drug discovery for tuberculosis
inferredcites →https://doi.org/10.1038/nrd2201openalex-snapshot
Drugs for bad bugs: confronting the challenges of antibacterial discovery
inferredcites →https://doi.org/10.1038/nrd3368openalex-snapshot
Impact of high-throughput screening in biomedical research
inferredcites →https://doi.org/10.1039/b917766copenalex-snapshot
A collaborative database and computational models for tuberculosis drug discovery
inferredcites →https://doi.org/10.1039/c0mb00104jopenalex-snapshot
Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis
inferredcites →https://doi.org/10.1039/c0md00129eopenalex-snapshot
Meta-analysis of molecular property patterns and filtering of public datasets of antimalarial “hits” and drugs
inferredcites →https://doi.org/10.1073/pnas.0802982105openalex-snapshot
In silico activity profiling reveals the mechanism of action of antimalarials discovered in a high-throughput screen
inferredcites →https://doi.org/10.1073/pnas.1018301108openalex-snapshot
Depletion of antibiotic targets has widely varying effects on growth
inferredcites →https://doi.org/10.1073/pnas.1118370109openalex-snapshot
Liver-stage malaria parasites vulnerable to diverse chemical scaffolds
inferredcites →https://doi.org/10.1096/fj.11-187401openalex-snapshot
Identifying apicoplast‐targeting antimalarials using high‐throughput compatible approaches
inferredcites →https://doi.org/10.1124/dmd.106.014613openalex-snapshot
Computational Approaches That Predict Metabolic Intermediate Complex Formation with CYP3A4 (+b5)
inferredcites →https://doi.org/10.1124/dmd.110.034918openalex-snapshot
Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties
inferredcites →https://doi.org/10.1126/science.1106753openalex-snapshot
A Diarylquinoline Drug Active on the ATP Synthase of Mycobacterium tuberculosis
inferredcites →https://doi.org/10.1128/aac.49.6.2153-2163.2005openalex-snapshot
New Small-Molecule Synthetic Antimycobacterials
inferredcites →https://doi.org/10.1146/annurev.pharmtox.45.120403.100120openalex-snapshot
THE MAGIC BULLETS AND TUBERCULOSIS DRUG TARGETS
inferredcites →https://doi.org/10.1177/1087057105281365openalex-snapshot
Using Extended-Connectivity Fingerprints with Laplacian-Modified Bayesian Analysis in High-Throughput Screening Follow-Up
inferredcites →https://doi.org/10.1186/1471-2210-12-1openalex-snapshot
Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets
inferredcites →https://doi.org/10.1186/1756-0500-4-504openalex-snapshot
Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets
inferredcites →https://doi.org/10.1371/journal.pone.0062325openalex-snapshot
Dispensing Processes Impact Apparent Biological Activity as Determined by Computational and Statistical Analyses
inferredcites →https://doi.org/10.1371/journal.pone.0063240openalex-snapshot
Enhancing Hit Identification in Mycobacterium tuberculosis Drug Discovery Using Validated Dual-Event Bayesian Models

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/9781119282594.ch1openalex-snapshot
Accessible Machine Learning Approaches for Toxicology
inferred← citeshttps://doi.org/10.1002/9781119282594.ch14openalex-snapshot
Developing Next Generation Tools for Computational Toxicology
inferred← citeshttps://doi.org/10.1002/9783527840748.ch13openalex-snapshot
Reliability and Applicability Assessment for Machine Learning Models
inferred← citeshttps://doi.org/10.1007/s10822-014-9762-yopenalex-snapshot
Bigger data, collaborative tools and the future of predictive drug discovery
inferred← citeshttps://doi.org/10.1007/s11095-015-1800-5openalex-snapshot
Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data
inferred← citeshttps://doi.org/10.1007/s11095-016-2029-7openalex-snapshot
The Next Era: Deep Learning in Pharmaceutical Research
inferred← citeshttps://doi.org/10.1007/s43441-025-00866-zopenalex-snapshot
Machine Learning in Tuberculosis Research: A Global Bibliometric Analysis of Diagnostic, Prognostic, and Drug Discovery Trends
inferred← citeshttps://doi.org/10.1016/j.drudis.2016.10.009openalex-snapshot
Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)
inferred← citeshttps://doi.org/10.1016/j.ijmedinf.2023.105241openalex-snapshot
Machine learning in medication prescription: A systematic review
inferred← citeshttps://doi.org/10.1016/j.iswa.2023.200197openalex-snapshot
Technology acceptance prediction of robo-advisors by machine learning
inferred← citeshttps://doi.org/10.1016/j.jmapro.2023.05.016openalex-snapshot
Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process
inferred← citeshttps://doi.org/10.1016/j.jmapro.2023.06.003openalex-snapshot
Reprint of: Explainable AI (XAI)-driven vibration sensing scheme for surface quality monitoring in a smart surface grinding process
inferred← citeshttps://doi.org/10.1016/j.nexres.2026.101842openalex-snapshot
Predictive modelling of drug release from PLGA microparticles using artificial intelligence and machine learning
inferred← citeshttps://doi.org/10.1016/j.tube.2022.102168openalex-snapshot
Mycobacterium abscessus drug discovery using machine learning
inferred← citeshttps://doi.org/10.1021/acs.chemrev.8b00728openalex-snapshot
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c00166openalex-snapshot
Quantum Machine Learning Algorithms for Drug Discovery Applications
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00143openalex-snapshot
Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00144openalex-snapshot
Open Source Bayesian Models. 2. Mining a “Big Dataset” To Create and Validate Models with ChEMBL
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00555openalex-snapshot
Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00004openalex-snapshot
Machine Learning Model Analysis and Data Visualization with Small Molecules Tested in a Mouse Model of Mycobacterium tuberculosis Infection (2014–2015)
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.3c01322openalex-snapshot
High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.9b02075openalex-snapshot
Molecule Property Analyses of Active Compounds for Mycobacterium tuberculosis
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.0c01013openalex-snapshot
Bioactivity Comparison across Multiple Machine Learning Algorithms Using over 5000 Datasets for Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.1c00791openalex-snapshot
Machine Learning Models for Mycobacterium tuberculosis In Vitro Activity: Prediction and Target Visualization
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.7b00578openalex-snapshot
Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.8b00083openalex-snapshot
Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery
inferred← citeshttps://doi.org/10.1021/acsinfecdis.9b00295openalex-snapshot
Intrabacterial Metabolism Obscures the Successful Prediction of an InhA Inhibitor of Mycobacterium tuberculosis
inferred← citeshttps://doi.org/10.1038/s42004-024-01220-4openalex-snapshot
The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications
inferred← citeshttps://doi.org/10.1080/23270012.2016.1141332openalex-snapshot
Big data analytics and big data science: a survey
inferred← citeshttps://doi.org/10.12688/f1000research.6181.1openalex-snapshot
Finding small molecules for the ‘next Ebola’
inferred← citeshttps://doi.org/10.12688/f1000research.7217.1openalex-snapshot
Machine learning models identify molecules active against the Ebola virus in vitro [version 1; referees: 2 approved]
inferred← citeshttps://doi.org/10.12688/f1000research.7217.2openalex-snapshot
Machine learning models identify molecules active against the Ebola virus in vitro
inferred← citeshttps://doi.org/10.12688/f1000research.7217.3openalex-snapshot
Machine learning models identify molecules active against the Ebola virus in vitro
inferred← citeshttps://doi.org/10.1371/journal.pntd.0003878openalex-snapshot
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inferred← citeshttps://doi.org/10.1371/journal.pone.0141076openalex-snapshot
Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery
inferred← citeshttps://doi.org/10.2174/1389200219666180820112457openalex-snapshot
Survey of Machine Learning Techniques in Drug Discovery
inferred← citeshttps://doi.org/10.3389/fchem.2021.614073openalex-snapshot
Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases
inferred← citeshttps://doi.org/10.3390/molecules25030456openalex-snapshot
Cinnamic Derivatives as Antitubercular Agents: Characterization by Quantitative Structure–Activity Relationship Studies
inferred← citeshttps://doi.org/10.5755/j01.eie.25.5.24361openalex-snapshot
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