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Updated: Apr 28, 2026

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Published on: October 23, 2019

Targeted kinase selectivity from kinase profiling data.

Francesca Milletti¹, Johannes C Hermann²

¹pRED Informatics, Roche , 340 Kingsland Street, Nutley, New Jersey 07110, United States.

ACS Medicinal Chemistry Letters

|June 6, 2014

Summary

This summary is machine-generated.

Leverage historical kinase profiling data to improve drug selectivity. This method uses chemical transformations between similar compounds to guide optimization for new kinase drug discovery projects.

Keywords:

Kinase inhibitors activity cliffs computational method kinase panels kinase selectivity matched pairs

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Last Updated: Apr 28, 2026

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

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Published on: October 23, 2019

Identification of Kinase-substrate Pairs Using High Throughput Screening

Identification of Kinase-substrate Pairs Using High Throughput Screening

Published on: August 29, 2015

Characterization at the Molecular Level using Robust Biochemical Approaches of a New Kinase Protein

Characterization at the Molecular Level using Robust Biochemical Approaches of a New Kinase Protein

Published on: June 30, 2019

Area of Science:

Medicinal Chemistry
Drug Discovery
Computational Chemistry

Background:

Kinase selectivity is crucial for successful drug design and kinase drug discovery programs.
Kinase profiling data is often underutilized in new drug discovery initiatives.
Optimizing selectivity is a key challenge in developing targeted therapies.

Purpose of the Study:

To present a novel method for optimizing compound selectivity profiles.
To utilize historical kinase profiling data for guiding drug design.
To demonstrate the transferability of chemical transformations across different compound scaffolds.

Main Methods:

Developing a computational method to analyze historical kinase profiling data.
Identifying pairs of similar compounds with differing kinase activities.
Proposing specific chemical transformations based on these compound pairs.
Validating the transferability of these transformations across diverse chemical structures.

Main Results:

The proposed method effectively suggests chemical modifications to enhance kinase selectivity.
Historical profiling data can be systematically exploited to optimize lead compounds.
Identified chemical transformations are applicable to unrelated series of compounds.
This approach facilitates the design of more selective kinase inhibitors.

Conclusions:

This method provides a valuable tool for optimizing kinase selectivity in drug discovery.
It enables the effective reuse of accumulated kinase profiling data for new projects.
The transferability of transformations across scaffolds broadens its applicability.
This strategy can accelerate the development of safer and more effective kinase-targeted drugs.