ArticleBriefings in bioinformatics2023
KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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
10 citing papers in PubMed.
- Single-cell and spatial transcriptomics analysis of osteoarthritis: pathway regulation, cell interaction networks, and therapeutic translation.Journal of translational medicine · 2026Review
- Multimodal interpretable deep learning for transcriptome-informed precision oncology and drug mechanism analysis.NPJ digital medicine · 2026Article
- Enhancing kinase-inhibitor activity and selectivity prediction through contrastive learning.Nature communications · 2025Article
- A meta-learning framework to mitigate negative transfer in transfer learning applicable to drug design.Scientific reports · 2025Article
- Enhancing accuracy of virtual kinase profiling via application of graph neural network to 3D pharmacophore ensembles.Journal of computer-aided molecular design · 2025Article
- Integrated multimodal hierarchical fusion and meta-learning for enhanced molecular property prediction.Briefings in bioinformatics · 2025Article
- InertDB as a generative AI-expanded resource of biologically inactive small molecules from PubChem.Journal of cheminformatics · 2025Article
- Modeling and Interpretability Study of the Structure-Activity Relationship for Multigeneration EGFR Inhibitors.ACS omega · 2025Article
- Combining graph neural networks and transformers for few-shot nuclear receptor binding activity prediction.Journal of cheminformatics · 2024Article
- KinomeMETA: a web platform for kinome-wide polypharmacology profiling with meta-learning.Nucleic acids research · 2024Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Kinase inhibitors are crucial in cancer treatment, but drug resistance and side effects hinder the development of effective drugs. To address these challenges, it is essential to analyze the polypharmacology of kinase inhibitor and identify compound with high selectivity profile. This study presents KinomeMETA, a framework for profiling the activity of small molecule kinase inhibitors across a panel of 661 kinases. By training a meta-learner based on a graph neural network and fine-tuning it to create kinase-specific learners, KinomeMETA outperforms benchmark multi-task models and other kinase profiling models. It provides higher accuracy for understudied kinases with limited known data and broader coverage of kinase types, including important mutant kinases. Case studies on the discovery of new scaffold inhibitors for membrane-associated tyrosine- and threonine-specific cdc2-inhibitory kinase and selective inhibitors for fibroblast growth factor receptors demonstrate the role of KinomeMETA in virtual screening and kinome-wide activity profiling. Overall, KinomeMETA has the potential to accelerate kinase drug discovery by more effectively exploring the kinase polypharmacology landscape.
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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.