ReviewACS chemical biology2011
Rational methods for the selection of diverse screening compounds.
Review in ACS chemical biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 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
46 citing papers in PubMed.
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- Overview of Computational Toxicology Methods Applied in Drug and Green Chemical Discovery.Journal of xenobiotics · 2024Review
- Natural product databases for drug discovery: Features and applications.Pharmaceutical science advances · 2024Review
- iSIM: instant similarity.Digital discovery · 2024Article
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- The Hitchhiker's Guide to Deep Learning Driven Generative Chemistry.ACS medicinal chemistry letters · 2023Review
- Creation of targeted compound libraries based on 3D shape recognition.Molecular diversity · 2023Article
- Exploration of a Large Virtual Chemical Space: Identification of Potent Inhibitors of Lactate Dehydrogenase-A against Pancreatic Cancer.Journal of chemical information and modeling · 2023Article
- Target identification for small-molecule discovery in the FOXO3a tumor-suppressor pathway using a biodiverse peptide library.Cell chemical biology · 2021Article
- Building Natural Product Libraries Using Quantitative Clade-Based and Chemical Clustering Strategies.mSystems · 2021Article
- BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space.International journal of molecular sciences · 2021Article
- A semi-supervised learning framework for quantitative structure-activity regression modelling.Bioinformatics (Oxford, England) · 2021Article
- Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products.Frontiers in chemistry · 2021Review
- A Screen for Small Molecules to TargetJournal of fungi (Basel, Switzerland) · 2020Article
- Large-Scale Virtual Screening Against the MET Kinase Domain Identifies a New Putative Inhibitor Type.Molecules (Basel, Switzerland) · 2020Article
- Development of a Novel Cell-Permeable Protein-Protein Interaction Inhibitor for the Polo-box Domain of Polo-like Kinase 1.ACS omega · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Traditionally a pursuit of large pharmaceutical companies, high-throughput screening assays are becoming increasingly common within academic and government laboratories. This shift has been instrumental in enabling projects that have not been commercially viable, such as chemical probe discovery and screening against high-risk targets. Once an assay has been prepared and validated, it must be fed with screening compounds. Crafting a successful collection of small molecules for screening poses a significant challenge. An optimized collection will minimize false positives while maximizing hit rates of compounds that are amenable to lead generation and optimization. Without due consideration of the relevant protein targets and the downstream screening assays, compound filtering and selection can fail to explore the great extent of chemical diversity and eschew valuable novelty. Herein, we discuss the different factors to be considered and methods that may be employed when assembling a structurally diverse compound collection for screening. Rational methods for selecting diverse chemical libraries are essential for their effective use in high-throughput screens.
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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.