ReviewFrontiers in genetics2014
Computational methods for analysis and inference of kinase/inhibitor relationships.
Review in Frontiers in genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 20 citations in OpenAlex.
- An atlas of bacterial serine-threonine kinases reveals functional diversity and key distinctions from eukaryotic kinases.Science signaling · 2025Article
- Poor Generalization by Current Deep Learning Models for Predicting Binding Affinities of Kinase Inhibitors.bioRxiv : the preprint server for biology · 2023Article
- Kinome-wide polypharmacology profiling of small molecules by multi-task graph isomorphism network approach.Acta pharmaceutica Sinica. B · 2023Article
- Kinome-wide identification of phosphorylation networks in eukaryotic proteomes.Bioinformatics (Oxford, England) · 2019Article
- Development of Certain Protein Kinase Inhibitors with the Components from Traditional Chinese Medicine.Frontiers in pharmacology · 2016Review
- A prismatic view of protein phosphorylation in health and disease.Frontiers in genetics · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The central role of kinases in virtually all signal transduction networks is the driving motivation for the development of compounds modulating their activity. ATP-mimetic inhibitors are essential tools for elucidating signaling pathways and are emerging as promising therapeutic agents. However, off-target ligand binding and complex and sometimes unexpected kinase/inhibitor relationships can occur for seemingly unrelated kinases, stressing that computational approaches are needed for learning the interaction determinants and for the inference of the effect of small compounds on a given kinase. Recently published high-throughput profiling studies assessed the effects of thousands of small compound inhibitors, covering a substantial portion of the kinome. This wealth of data paved the road for computational resources and methods that can offer a major contribution in understanding the reasons of the inhibition, helping in the rational design of more specific molecules, in the in silico prediction of inhibition for those neglected kinases for which no systematic analysis has been carried yet, in the selection of novel inhibitors with desired selectivity, and offering novel avenues of personalized therapies.
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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.