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Modeling Kinase Inhibition Using Highly Confident Data Sets.

Sorin Avram1, Alina Bora1, Liliana Halip1

  • 1Department of Computational Chemistry , Institute of Chemistry Timişoara of Romanian Academy , 24 Mihai Viteazu Avenue , 300223 - Timişoara , Romania.

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This study enhances kinase inhibitor prediction models by comparing extended connectivity fingerprints (ECFP4) and pharmacophore fingerprints (PFPs). Combining these (PFPECFP) significantly improves virtual screening performance for drug discovery.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Protein kinases are crucial drug targets, necessitating accurate prediction of small molecule activities.
  • Previous work established predictive models for kinase inhibitors, with this study building upon that foundation.
  • Ligand-based prediction models are essential for efficient profiling of kinase inhibitors.

Purpose of the Study:

  • To build and evaluate ligand-based kinase inhibitor prediction models.
  • To analyze kinase-inhibitor data from the ChEMBL database, identifying sources of inconsistency.
  • To compare the performance of different molecular fingerprints in predicting kinase inhibitor activity.

Main Methods:

  • Analysis of kinase-inhibitor pairs with multiple activity points from the ChEMBL database.
  • Modeling of 104 kinase activities using unbiased, target-specific activity points.
  • Comparison of extended connectivity fingerprints (ECFP4) and 2D pharmacophore fingerprints (PFPs) using tolerance intervals (TIs) in virtual screening (VS) and classification tasks.
  • Evaluation using random (RandSets) and diversity-based (DivSets) test sets.

Main Results:

  • Lower IC50 values are more reliable for quantitative structure-activity relationship (QSAR) studies.
  • ECFP4 and PFP models showed differential performance across kinases in VS, with PFPs excelling in classifying active compounds (higher sensitivity).
  • Combining ECFP4 and PFPs into PFPECFP significantly enhanced VS performance (eROCE) for most kinases, particularly in challenging diversity-based test sets.

Conclusions:

  • The PFPECFP combined encoding offers superior performance for kinase inhibitor prediction, especially in virtual screening.
  • Understanding the limitations of individual kinase predictors is key to improving virtual screening and discovering novel compounds.
  • This work advances the development of predictive models to aid in the discovery of compounds with desired polypharmacology.