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Updated: Dec 23, 2025

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

Successive Statistical and Structure-Based Modeling to Identify Chemically Novel Kinase Inhibitors.

Lindsey Burggraaff¹, Eelke B Lenselink¹, Willem Jespers^1,2

¹Division of Drug Discovery & Safety, Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands.

Journal of Chemical Information and Modeling

|April 29, 2020

Summary

This study introduces a novel workflow for kinase inhibitor design, successfully identifying new RET kinase inhibitors. This approach aids in discovering novel chemical matter for multitarget drug design and polypharmacology modeling.

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Last Updated: Dec 23, 2025

Identification of Novel CK2 Kinase Substrates Using a Versatile Biochemical Approach

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Published on: February 21, 2019

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

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Identification of Kinase-substrate Pairs Using High Throughput Screening

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Published on: August 29, 2015

Area of Science:

Medicinal Chemistry
Computational Drug Discovery
Pharmacology

Background:

Kinases play crucial roles in cellular processes and are key targets for anticancer drug development.
Multitarget drug design aims to inhibit multiple kinases simultaneously for enhanced therapeutic effects.
Developing selective kinase inhibitors requires sophisticated modeling approaches.

Purpose of the Study:

To present and validate a computational workflow for modeling kinase bioactivity spectra.
To identify novel kinase inhibitors with specific selectivity profiles.
To explore the application of this workflow in multitarget drug design and polypharmacology.

Main Methods:

Development and benchmarking of statistical and structure-based models for kinase inhibition.
Virtual screening using the developed workflow to identify inhibitors for RET kinase.
Experimental validation of identified inhibitors for RET kinase activity and potency.

Main Results:

The workflow successfully identified 5 novel and chemically diverse RET kinase inhibitors.
The most potent inhibitor exhibited modest activity with a pIC50 value of 5.1.
Inhibitors showed low similarity to known RET inhibitors, indicating novel chemical matter.

Conclusions:

The presented multitarget workflow effectively detects novel kinase inhibitors.
This approach is applicable to polypharmacology modeling and identifying new chemical matter for existing targets.
The workflow can be readily adapted for other kinase targets in drug discovery.