ArticleJournal of chemical information and modeling2016
Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound-Kinase Activities: A Way toward Selective Promiscuity by Design?
Article in Journal of chemical information and modeling, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- A novel framework for the discovery of MAPK-activated protein kinase 2 (MAPKAPK2) inhibitors using a multi-feature deep learning ensemble.Journal of computer-aided molecular design · 2026Article
- A Computational Framework for the Design and Development of Isoform Selective PI3Kα Inhibitors as Novel Anticancer Agents.Molecules (Basel, Switzerland) · 2026Article
- Integrating Graph Convolution and Attention Mechanism for Kinase Inhibition Prediction.Molecules (Basel, Switzerland) · 2025Article
- Poor Generalization by Current Deep Learning Models for Predicting Binding Affinities of Kinase Inhibitors.bioRxiv : the preprint server for biology · 2023Article
- Predicting the target landscape of kinase inhibitors using 3D convolutional neural networks.PLoS computational biology · 2023Article
- Large-Scale Modeling of Sparse Protein Kinase Activity Data.Journal of chemical information and modeling · 2023Article
- Establishment of extensive artificial intelligence models for kinase inhibitor prediction: Identification of novel PDGFRB inhibitors.Computers in biology and medicine · 2023Article
- Proteochemometric Modeling Identifies Chemically Diverse Norepinephrine Transporter Inhibitors.Journal of chemical information and modeling · 2023Article
- Papyrus: a large-scale curated dataset aimed at bioactivity predictions.Journal of cheminformatics · 2023Article
- KUALA: a machine learning-driven framework for kinase inhibitors repositioning.Scientific reports · 2022Article
- Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches.Science China. Life sciences · 2022Article
- Sequence-based prediction of protein binding regions and drug-target interactions.Journal of cheminformatics · 2022Article
- A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease.eLife · 2022Article
- Exploration and augmentation of pharmacological space via adversarial auto-encoder model for facilitating kinase-centric drug development.Journal of cheminformatics · 2021Article
- Article
- The Kinase Chemogenomic Set (KCGS): An Open Science Resource for Kinase Vulnerability Identification.International journal of molecular sciences · 2021Article
- Is Structure-Based Drug Design Ready for Selectivity Optimization?Journal of chemical information and modeling · 2020Article
- Ibrutinib-Mediated Atrial Fibrillation Attributable to Inhibition of C-Terminal Src Kinase.Circulation · 2020Article
- Successive Statistical and Structure-Based Modeling to Identify Chemically Novel Kinase Inhibitors.Journal of chemical information and modeling · 2020Article
- Developing a Kinase-Specific Target Selection Method Using a Structure-Based Machine Learning Approach.Advances and applications in bioinformatics and chemistry : AABC · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Drug discovery programs frequently target members of the human kinome and try to identify small molecule protein kinase inhibitors, primarily for cancer treatment, additional indications being increasingly investigated. One of the challenges is controlling the inhibitors degree of selectivity, assessed by in vitro profiling against panels of protein kinases. We manually extracted, compiled, and standardized such profiles published in the literature: we collected 356 908 data points corresponding to 482 protein kinases, 2106 inhibitors, and 661 patents. We then analyzed this data set in terms of kinome coverage, results reproducibility, popularity, and degree of selectivity of both kinases and inhibitors. We used the data set to create robust proteochemometric models capable of predicting kinase activity (the ligand-target space was modeled with an externally validated RMSE of 0.41 ± 0.02 log units and R02 0.74 ± 0.03), in order to account for missing or unreliable measurements. The influence on the prediction quality of parameters such as number of measurements, Murcko scaffold frequency or inhibitor type was assessed. Interpretation of the models enabled to highlight inhibitors and kinases properties correlated with higher affinities, and an analysis in the context of kinases crystal structures was performed. Overall, the models quality allows the accurate prediction of kinase-inhibitor activities and their structural interpretation, thus paving the way for the rational design of compounds with a targeted selectivity profile.
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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.