ArticleJournal of chemical information and modeling2013
Kinome-wide activity modeling from diverse public high-quality data sets.
Article in Journal of chemical information and modeling, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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.
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Who cites it
25 citing papers in PubMed.
- Design of novel PI3Kα and PI3Kγ inhibitors for cancer treatment using pharmacophore, protein-ligand contacts, and machine learning methods.Journal of computer-aided molecular design · 2026Article
- MEGDTA: multi-modal drug-target affinity prediction based on protein three-dimensional structure and ensemble graph neural network.BMC genomics · 2025Article
- KinomeMETA: a web platform for kinome-wide polypharmacology profiling with meta-learning.Nucleic acids research · 2024Article
- Kinome-Wide Virtual Screening by Multi-Task Deep Learning.International journal of molecular sciences · 2024Article
- Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors.Journal of cheminformatics · 2024Article
- KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling.Briefings in bioinformatics · 2023Article
- AI-Assisted chemical probe discovery for the understudied Calcium-Calmodulin Dependent Kinase, PNCK.PLoS computational biology · 2023Article
- A novel chemical attack on Notch-mediated transcription by targeting the NACK ATPase.Molecular therapy oncolytics · 2023Article
- Improving the Prediction of Potential Kinase Inhibitors with Feature Learning on Multisource Knowledge.Interdisciplinary sciences, computational life sciences · 2022Article
- Elucidating direct kinase targets of compound Danshen dropping pills employing archived data and prediction models.Scientific reports · 2021Article
- Machine and deep learning approaches for cancer drug repurposing.Seminars in cancer biology · 2021Review
- LINCS Data Portal 2.0: next generation access point for perturbation-response signatures.Nucleic acids research · 2020Article
- Use of machine learning to identify novel, behaviorally active antagonists of the insect odorant receptor co-receptor (Orco) subunit.Scientific reports · 2019Article
- Drug Repositioning in Glioblastoma: A Pathway Perspective.Frontiers in pharmacology · 2018Review
- SimBoost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines.Journal of cheminformatics · 2017Article
- Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound-Kinase Activities: A Way toward Selective Promiscuity by Design?Journal of chemical information and modeling · 2016Article
- High quality, small molecule-activity datasets for kinase research.F1000Research · 2016Article
- Large-Scale Computational Screening Identifies First in Class Multitarget Inhibitor of EGFR Kinase and BRD4.Scientific reports · 2015Article
- Rational Polypharmacology: Systematically Identifying and Engaging Multiple Drug Targets To Promote Axon Growth.ACS chemical biology · 2015Article
- Screening of cell cycle fusion proteins to identify kinase signaling networks.Cell cycle (Georgetown, Tex.) · 2015Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Large corpora of kinase small molecule inhibitor data are accessible to public sector research from thousands of journal article and patent publications. These data have been generated employing a wide variety of assay methodologies and experimental procedures by numerous laboratories. Here we ask the question how applicable these heterogeneous data sets are to predict kinase activities and which characteristics of the data sets contribute to their utility. We accessed almost 500,000 molecules from the Kinase Knowledge Base (KKB) and after rigorous aggregation and standardization generated over 180 distinct data sets covering all major groups of the human kinome. To assess the value of the data sets, we generated hundreds of classification and regression models. Their rigorous cross-validation and characterization demonstrated highly predictive classification and quantitative models for the majority of kinase targets if a minimum required number of active compounds or structure-activity data points were available. We then applied the best classifiers to compounds most recently profiled in the NIH Library of Integrated Network-based Cellular Signatures (LINCS) program and found good agreement of profiling results with predicted activities. Our results indicate that, although heterogeneous in nature, the publically accessible data sets are exceedingly valuable and well suited to develop highly accurate predictors for practical Kinome-wide virtual screening applications and to complement experimental kinase profiling.
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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.