ArticleFrontiers in molecular biosciences2025
DeepKinome: quantitative prediction of kinase binding affinity by a compound using deep learning based regression model.
Article in Frontiers in molecular biosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- DeepKinomeWeb: a quantitative, panel-level platform for kinase inhibitor screening and selectivity profiling.Nucleic acids research · 2026Article
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4 authors.
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Abstract
Introduction: Kinases are essential for cellular regulation and drug development. Predicting the quantitative binding affinity between small-molecule compounds and kinases remains a challenge because of data complexity. Method: We developed DeepKinome, a 20-layer convolutional neural network-based deep learning (DL) regression model, to predict quantitative binding affinity. Given the continuous nature of binding affinity, the root mean square error (RMSE), the coefficient of determination (R Results: DeepKinome outperformed five DL and four machine learning models, achieving an RMSE of 1.157, an R Conclusion: DeepKinome offers a promising approach for understanding kinase inhibition and compound binding.
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