ArticleiScience2018
KInhibition: A Kinase Inhibitor Selection Portal.
Article in iScience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 20 citations in OpenAlex.
- Comprehensive profiling of clinically approved kinase inhibitors reveals mutation-specific inhibitors and opportunities for drug repurposing.Nature biotechnology · 2026Article
- Computational discovery of novel FYN kinase inhibitors: a cheminformatics and machine learning-driven approach to targeted cancer and neurodegenerative therapy.Molecular diversity · 2024Article
- NCT/DKFZ MASTER handbook of interpreting whole-genome, transcriptome, and methylome data for precision oncology.NPJ precision oncology · 2023Article
- Polypharmacology-based kinome screen identifies new regulators of KSHV reactivation.PLoS pathogens · 2023Article
- Strategy toward Kinase-Selective Drug Discovery.Journal of chemical theory and computation · 2023Article
- Transcriptome profiling for precision cancer medicine using shallow nanopore cDNA sequencing.Scientific reports · 2023Article
- Polypharmacology-based kinome screen identifies new regulators of KSHV reactivation.bioRxiv : the preprint server for biology · 2023Article
- Target-specific compound selectivity for multi-target drug discovery and repurposing.Frontiers in pharmacology · 2022Article
- Knowledge bases and software support for variant interpretation in precision oncology.Briefings in bioinformatics · 2021Article
- Utilizing preclinical models to develop targeted therapies for rare central nervous system cancers.Neuro-oncology · 2021Review
- Computational modeling identifies multitargeted kinase inhibitors as effective therapies for metastatic, castration-resistant prostate cancer.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Nanomedicine of tyrosine kinase inhibitors.Theranostics · 2021Review
- Non-linear Deep Neural Network for Rapid and Accurate Prediction of Phenotypic Responses to Kinase Inhibitors.iScience · 2020Article
- The therapeutic potential of Aurora kinases targeting in glioblastoma: from preclinical research to translational oncology.Journal of molecular medicine (Berlin, Germany) · 2020Review
- Organotypic tumor slice cultures provide a versatile platform for immuno-oncology and drug discovery.OncoimmunologyArticle
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Protein kinases constitute a large class of signaling molecules frequently targeted in research and clinical uses. However, kinase inhibitors are notoriously non-specific, making it difficult to select an appropriate inhibitor for a given kinase. Available data from large-scale kinase inhibitor screens are often difficult to query. Here, we present KInhibition (https://kinhibition.fredhutch.org), an online portal that allows users to search publicly available datasets to find selective inhibitors for a chosen kinase or group of kinases. Compounds are sorted by a KInhibition Selectivity Score, calculated based on compounds' activity against the selected kinase(s) versus activity against all other kinases for which that compound has been profiled. The current version allows users to query four datasets, with a framework that can easily accommodate additional datasets. KInhibition represents a powerful platform through which researchers from broad areas of biology, chemistry, and pharmacology can easily interrogate large datasets to help guide their selection of kinase inhibitors.
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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.