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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Related Experiment Video

Updated: Mar 28, 2026

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
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Computational Modeling of Kinase Inhibitor Selectivity.

Govindan Subramanian1, Manish Sud2

  • 1Structure, Design and Informatics, sanofi-aventis U.S., 1041 Route 202-206, P.O. Box 6800, Bridgewater, New Jersey 08807.

ACS Medicinal Chemistry Letters
|December 18, 2015
PubMed
Summary
This summary is machine-generated.

This study predicts off-target kinase interactions for 15 therapeutic kinase inhibitors. Computational predictions show high accuracy, enabling potential drug repurposing for new kinase targets.

Keywords:
Kinase selectivitybinding hot spotsbinding site signaturecomputational predictionkinase conformationkinase inhibitors

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Area of Science:

  • Pharmacology
  • Computational Biology
  • Medicinal Chemistry

Background:

  • Kinase inhibitors are crucial therapeutics, but off-target effects can cause toxicity.
  • Understanding kinase inhibitor selectivity is essential for drug development and repurposing.

Purpose of the Study:

  • To computationally predict off-target kinase interactions for 15 therapeutic kinase inhibitors.
  • To validate predictions against available experimental data and assess accuracy.
  • To explore the potential for repurposing known kinase inhibitors to new targets.

Main Methods:

  • Utilized computational methods to predict inhibitor selectivity against approximately 480 human kinases.
  • Compared predictions with experimental data for around 280 kinase targets.
  • Validated predictive models using recent experimental data for sorafenib and sunitinib.

Main Results:

  • Achieved an average prediction accuracy and specificity of approximately 90% for kinase off-target interactions.
  • Demonstrated high predictive accuracy for sorafenib and sunitinib against new experimental data.
  • Successfully predicted interactions across the human kinome.

Conclusions:

  • Computational predictions of kinase inhibitor selectivity are highly accurate.
  • This approach facilitates the identification of new therapeutic opportunities by repurposing existing kinase inhibitors.
  • The findings support the development of targeted therapies with reduced off-target effects.