ArticleACS chemical biology2017
Linking High-Throughput Screens to Identify MoAs and Novel Inhibitors of Mycobacterium tuberculosis Dihydrofolate Reductase.
Article in ACS chemical biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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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
9 citing papers in PubMed, 28 citations in OpenAlex.
- Preparation of Lipophilic Derivatives ofACS omega · 2025Article
- Scaffold Hopping Strategy toward New 4‑Aminoquinazolines Active Against Extracellular and Intracellular.ACS medicinal chemistry letters · 2025Article
- Redirecting raltitrexed from cancer cell thymidylate synthase toScience advances · 2024Article
- Pharmacological validation of dihydrofolate reductase as a drug target inAntimicrobial agents and chemotherapy · 2024Article
- Artificial intelligence applications in the diagnosis and treatment of bacterial infections.Frontiers in microbiology · 2024Review
- Sensing of Antibiotic-Bacteria Interactions.Antibiotics (Basel, Switzerland) · 2023Review
- Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases.Frontiers in chemistry · 2021Review
- A high-throughput whole cell screen to identify inhibitors of Mycobacterium tuberculosis.PloS one · 2019Article
- Rapidly evolving changes and gene loss associated with host switching in Corynebacterium pseudotuberculosis.PloS one · 2018Article
Corrections and comments
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Authors and funding
18 authors at 2 institutions in 1 country.
Funding
Abstract
Though phenotypic and target-based high-throughput screening approaches have been employed to discover new antibiotics, the identification of promising therapeutic candidates remains challenging. Each approach provides different information, and understanding their results can provide hypotheses for a mechanism of action (MoA) and reveal actionable chemical matter. Here, we describe a framework for identifying efficacy targets of bioactive compounds. High throughput biophysical profiling against a broad range of targets coupled with machine learning was employed to identify chemical features with predicted efficacy targets for a given phenotypic screen. We validate the approach on data from a set of 55 000 compounds in 24 historical internal antibacterial phenotypic screens and 636 bacterial targets screened in high-throughput biophysical binding assays. Models were built to reveal the relationships between phenotype, target, and chemotype, which recapitulated mechanisms for known antibacterials. We also prospectively identified novel inhibitors of dihydrofolate reductase with nanomolar antibacterial efficacy against Mycobacterium tuberculosis. Molecular modeling provided structural insight into target-ligand interactions underlying selective killing activity toward mycobacteria over human cells.
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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.