ArticlePharmaceutical research2014
Combining computational methods for hit to lead optimization in Mycobacterium tuberculosis drug discovery.
Article in Pharmaceutical research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Global Research Trends and Insights on AI Usage in Tuberculosis: Bibliometric Analysis.JMIR medical informatics · 2026Article
- Machine Learning in Tuberculosis Research: A Global Bibliometric Analysis of Diagnostic, Prognostic, and Drug Discovery Trends.Therapeutic innovation & regulatory science · 2026Article
- Machine Learning Models forMolecular pharmaceutics · 2022Article
- Mycobacterium abscessus drug discovery using machine learning.Tuberculosis (Edinburgh, Scotland) · 2022Article
- 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
- libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications.Journal of chemical information and modeling · 2020Article
- Intrabacterial Metabolism Obscures the Successful Prediction of an InhA Inhibitor ofACS infectious diseases · 2019Article
- A Useful Synthesis of 2-Acylamino-1,3,4-oxadiazoles from Acylthiosemicarbazides Using Potassium Iodate and the Discovery of New Antibacterial Compounds.Molecules (Basel, Switzerland) · 2019Article
- Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.Molecular pharmaceutics · 2018Article
- Naïve Bayesian Models for Vero Cell Cytotoxicity.Pharmaceutical research · 2018Article
- Data Mining and Computational Modeling of High-Throughput Screening Datasets.Methods in molecular biology (Clifton, N.J.) · 2018Article
- Addressing the Metabolic Stability of Antituberculars through Machine Learning.ACS medicinal chemistry letters · 2017Article
- Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB).Drug discovery today · 2017Review
- 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
- COTI-2, a novel small molecule that is active against multiple human cancer cell lines in vitro and in vivo.Oncotarget · 2016Article
- Predictive modeling targets thymidylate synthase ThyX in Mycobacterium tuberculosis.Scientific reports · 2016Article
- Putative histidine kinase inhibitors with antibacterial effect against multi-drug resistant clinical isolates identified by in vitro and in silico screens.Scientific reports · 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
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeTuberculosis treatments need to be shorter and overcome drug resistance. Our previous large scale phenotypic high-throughput screening against Mycobacterium tuberculosis (Mtb) has identified 737 active compounds and thousands that are inactive. We have used this data for building computational models as an approach to minimize the number of compounds tested.
methodsA cheminformatics clustering approach followed by Bayesian machine learning models (based on publicly available Mtb screening data) was used to illustrate that application of these models for screening set selections can enrich the hit rate.
resultsIn order to explore chemical diversity around active cluster scaffolds of the dose-response hits obtained from our previous Mtb screens a set of 1924 commercially available molecules have been selected and evaluated for antitubercular activity and cytotoxicity using Vero, THP-1 and HepG2 cell lines with 4.3%, 4.2% and 2.7% hit rates, respectively. We demonstrate that models incorporating antitubercular and cytotoxicity data in Vero cells can significantly enrich the selection of non-toxic actives compared to random selection. Across all cell lines, the Molecular Libraries Small Molecule Repository (MLSMR) and cytotoxicity model identified ~10% of the hits in the top 1% screened (>10 fold enrichment). We also showed that seven out of nine Mtb active compounds from different academic published studies and eight out of eleven Mtb active compounds from a pharmaceutical screen (GSK) would have been identified by these Bayesian models.
conclusionCombining clustering and Bayesian models represents a useful strategy for compound prioritization and hit-to lead optimization of antitubercular agents.
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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.