ArticleChemistry & biology2013
Bayesian models leveraging bioactivity and cytotoxicity information for drug discovery.
Article in Chemistry & biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
67 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Antimicrobial resistance crisis: could artificial intelligence be the solution?Military Medical Research · 2024Pooled it
- Global Research Trends and Insights on AI Usage in Tuberculosis: Bibliometric Analysis.JMIR medical informatics · 2026Article
- Natural products as drug leads: exploring their potential in drug discovery and development.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Exploring the Role of Chemoinformatics in Accelerating Drug Discovery: A Computational Approach.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Recent Applications of Artificial Intelligence in Discovery of New Antibacterial Agents.Advances and applications in bioinformatics and chemistry : AABC · 2024Review
- High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents.Journal of medicinal chemistry · 2023Article
- Using a Graph Convolutional Neural Network Model to Identify Bile Salt Export Pump Inhibitors.ACS omega · 2023Article
- Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.Molecular diversity · 2023Review
- Random Forest Model Predictions Afford Dual-Stage Antimalarial Agents.ACS infectious diseases · 2022Article
- Article
- Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery.Journal of computer-aided molecular design · 2022Article
- Machine Learning Models forMolecular pharmaceutics · 2022Article
- The Chemical Property Position of Bedaquiline Construed by a Chemical Global Positioning System-Natural Product.Molecules (Basel, Switzerland) · 2022Article
- Mycobacterium abscessus drug discovery using machine learning.Tuberculosis (Edinburgh, Scotland) · 2022Article
- Recent advances in drug repurposing using machine learning.Current opinion in chemical biology · 2021Review
- Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus.Journal of chemical information and modeling · 2021Article
- Bayesian Modeling and Intrabacterial Drug Metabolism Applied to Drug-ResistantACS infectious diseases · 2021Article
- Discovery of 5-Nitro-6-thiocyanatopyrimidines as Inhibitors ofACS medicinal chemistry letters · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Random Forest Model Prediction of Compound Oral Exposure in the Mouse.ACS pharmacology & translational science · 2021Article
7 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Identification of unique leads represents a significant challenge in drug discovery. This hurdle is magnified in neglected diseases such as tuberculosis. We have leveraged public high-throughput screening (HTS) data to experimentally validate a virtual screening approach employing Bayesian models built with bioactivity information (single-event model) as well as bioactivity and cytotoxicity information (dual-event model). We virtually screened a commercial library and experimentally confirmed actives with hit rates exceeding typical HTS results by one to two orders of magnitude. This initial dual-event Bayesian model identified compounds with antitubercular whole-cell activity and low mammalian cell cytotoxicity from a published set of antimalarials. The most potent hit exhibits the in vitro activity and in vitro/in vivo safety profile of a drug lead. These Bayesian models offer significant economies in time and cost to drug discovery.
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What OpenQuestion holds
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.