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Updated: Nov 26, 2025

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Published on: October 23, 2019

Developing a Kinase-Specific Target Selection Method Using a Structure-Based Machine Learning Approach.

Arina Afanasyeva¹, Chioko Nagao^1,2, Kenji Mizuguchi^1,2

¹Bioinformatics Project, National Institutes of Biomedical Innovation, Health and Nutrition, Osaka, Japan.

Advances and Applications in Bioinformatics and Chemistry : AABC

|December 9, 2020

Summary

This summary is machine-generated.

Developing selective kinase inhibitors is challenging due to similar ATP-binding pockets. This study introduces a machine learning model using structure-based descriptors for accurate kinase target prioritization, aiding novel drug discovery.

Keywords:

activity prediction docking interaction descriptors kinase machine learning

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Last Updated: Nov 26, 2025

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Identification of Novel CK2 Kinase Substrates Using a Versatile Biochemical Approach

Identification of Novel CK2 Kinase Substrates Using a Versatile Biochemical Approach

Published on: February 21, 2019

Area of Science:

Computational chemistry
Drug discovery
Bioinformatics

Background:

Developing selective kinase inhibitors is difficult due to structural similarities in ATP-binding pockets.
Kinase inhibitors are crucial for treating various diseases but achieving selectivity remains a challenge.

Purpose of the Study:

To design a machine learning model for improved characterization of protein-ligand interactions.
To develop a computational method for accurate kinase target prioritization.

Main Methods:

Utilized a dataset of 104 human kinases with PDB structures and activity data against 1202 compounds.
Employed structure-based and energy-based descriptors to build an activity-predicting machine learning model.
Proposed novel structure-based interaction descriptors for enhanced prediction accuracy.

Main Results:

Achieved high accuracy in kinase target prioritization compared to existing structure-based methods.
Demonstrated consistent prediction accuracy on structurally diverse compounds, indicating unbiased performance.
The developed model effectively predicts kinase activity and prioritizes targets.

Conclusions:

The ligand-oriented computational method offers accurate kinase target prioritization.
The approach is unbiased by ligand structural similarity in training data.
This method can aid in the development of novel, highly selective kinase inhibitors.