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Related Experiment Video

Updated: Jun 17, 2026

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
13:22

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Published on: October 23, 2019

Structure-guided expansion of kinase fragment libraries driven by support vector machine models.

Jon A Erickson1, Mary M Mader, Ian A Watson

  • 1Eli Lilly and Company, Lilly Research Laboratories, Lilly Corporate Center, Indianapolis, IN 46285, USA. jae@lilly.com

Biochimica Et Biophysica Acta
|December 17, 2009
PubMed
Summary

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This study introduces a novel de novo design process for creating kinase inhibitor libraries using quantitative structure-activity relationship (QSAR) models and fragment-based drug design (FBDD) principles. The validated process successfully generated seven new libraries with a 92% hit rate against six kinases.

Area of Science:

  • Medicinal Chemistry
  • Computational Chemistry
  • Drug Discovery

Background:

  • Developing novel kinase inhibitors is crucial for targeted therapies.
  • Existing drug design methods require optimization for efficiency and novelty.
  • Fragment-based drug design (FBDD) and quantitative structure-activity relationship (QSAR) modeling offer powerful tools for inhibitor design.

Purpose of the Study:

  • To outline a novel de novo design process for creating diverse kinase inhibitor libraries.
  • To leverage QSAR models and FBDD principles for efficient scaffold and substituent selection.
  • To validate the predictive power of the designed models and libraries.

Main Methods:

  • Utilized a profiling paradigm to generate extensive kinase inhibitor data for QSAR model construction.

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Last Updated: Jun 17, 2026

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
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Published on: October 23, 2019

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Published on: August 29, 2015

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  • Incorporated diverse X-ray structure information for accurate binding mode prediction.
  • Employed FBDD principles, including fragmenting known actives and model-driven recombination.
  • Developed QSAR models using support vector machine (SVM) with Merck atom pair fingerprints from 6 kinase assays.
  • Prospectively qualified models using internal compound collections, achieving 82% hit rate and 2.5% enrichment.

    • Main Results:

    • The developed QSAR models were prospectively validated with high hit and enrichment rates.
    • Seven novel kinase inhibitor libraries were designed, synthesized, and tested against six kinases.
    • The designed libraries demonstrated a high overall hit rate of 92% for 179 compounds.
    • Analysis of substituent frequency and specific library designs provided further insights.

    Conclusions:

    • The novel de novo design process is effective for generating high-quality kinase inhibitor libraries.
    • The integration of QSAR modeling and FBDD principles enhances drug discovery efficiency.
    • The validated approach provides a robust platform for future inhibitor library design and optimization.