ArticleMolecular pharmaceutics2018
Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.
Article in Molecular pharmaceutics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 73 papers.
What it found
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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.
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.
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Who cites it
73 citing papers in PubMed.
- Global Research Trends and Insights on AI Usage in Tuberculosis: Bibliometric Analysis.JMIR medical informatics · 2026Article
- Uncertainty Quantification with Domain Classification Models for Acetylcholinesterase Inhibition.Journal of chemical information and modeling · 2026Article
- Efficacy, Structure-Activity Relationship, and Mode of Action Studies of a New Generation of Acridine/Acridone-Based Antimalarials.ACS infectious diseases · 2026Article
- Machine learning-based prediction of intellectual disability in children with autism spectrum disorder: using behavioral observation techniques.BMC psychiatry · 2026Article
- Antimicrobial Resistance: The Answers.British journal of biomedical science · 2026Review
- Machine Learning in Tuberculosis Research: A Global Bibliometric Analysis of Diagnostic, Prognostic, and Drug Discovery Trends.Therapeutic innovation & regulatory science · 2026Article
- Development of machine learning models to identify potentially active compounds against tuberculosis.Scientific reports · 2025Article
- MegaEye: Applying multiple machine learning approaches to identify oral compounds with ocular bioactivity.Artificial intelligence in the life sciences · 2025Article
- Inhibition of equilibrative nucleoside transporters 1 and 2 by proteolysis-targeting chimeras (PROTACs).The Journal of pharmacology and experimental therapeutics · 2025Article
- Applicability domain-expansion studies for machine learning models reveal new inhibitors of CYP2B6.Drug metabolism and disposition: the biological fate of chemicals · 2025Article
- DDI-CYP: Metabolism ensemble models for drug-drug interaction predictions.Drug metabolism and disposition: the biological fate of chemicals · 2025Article
- Machine Learning and Large Language Models for Modeling Complex Toxicity Pathways and Predicting Steroidogenesis.Environmental science & technology · 2025Article
- Article
- Computational Approaches for Predicting Drug Interactions with Human Organic Anion Transporter 4 (OAT4).Molecular pharmaceutics · 2025Article
- Discovery of Dual Targeting GSK-3β/HIV-1 Reverse Transcriptase Inhibitors as Neuroprotective Antiviral Agents.ACS chemical neuroscience · 2025Article
- Repurposing lapatinib as a triple antagonist of chemokine receptors 3, 4, and 5.Molecular pharmacology · 2025Article
- Machine learning-enabled virtual screening indicates the anti-tuberculosis activity of aldoxorubicin and quarfloxin with verification by molecular docking, molecular dynamics simulations, and biological evaluations.Briefings in bioinformatics · 2024Article
- TamGen: drug design with target-aware molecule generation through a chemical language model.Nature communications · 2024Article
- Predicting the Hallucinogenic Potential of Molecules Using Artificial Intelligence.ACS chemical neuroscience · 2024Article
- Near-Term Quantum Classification Algorithms Applied to Antimalarial Drug Discovery.Journal of chemical information and modeling · 2024Article
13 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
Abstract
Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phenotypic screens and many thousands of new active compounds identified in vitro. However, with limited funding, efforts to discover new active molecules against Mtb needs to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule Mtb data and developed new models with a total of 18,886 molecules with activity cutoffs of 10 μM, 1 μM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One Mtb model, a combined in vitro and in vivo data Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set ( n = 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature Mtb data generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such Mtb machine learning models could help prioritize compounds for testing in vitro and in vivo.
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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.