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KUALA: a machine learning-driven framework for kinase inhibitors repositioning.

Giada De Simone1, Davide Stefano Sardina2, Maria Rita Gulotta3

  • 1Molecular Informatics Group, Fondazione Ri.MED, Via Filippo Marini 14, 90128, Palermo, Italy. gdesimone@fondazionerimed.com.

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|October 25, 2022
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Summary
This summary is machine-generated.

This study introduces KUALA, a machine learning framework to identify kinase active ligands. KUALA provides scores for drug repurposing and multi-target effects, aiding in novel therapeutic development.

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Area of Science:

  • Biochemistry and Molecular Biology
  • Pharmacology and Drug Discovery
  • Computational Chemistry

Background:

  • Protein kinases are crucial drug targets for diseases like cancer, cardiovascular, and inflammatory conditions.
  • High similarity in kinase binding sites presents challenges for drug selectivity but offers opportunities for poly-pharmacology and drug repositioning.
  • Developing selective and effective kinase inhibitors remains a significant challenge in pharmaceutical research.

Purpose of the Study:

  • To develop an automated machine learning framework (KUALA) for identifying kinase-active ligands.
  • To provide a multi-target priority score and a drug repurposing threshold for kinase ligands.
  • To facilitate the discovery of novel therapeutics and drug repositioning strategies for kinase-related diseases.

Main Methods:

  • Utilized a machine learning approach incorporating specific molecular descriptors.
  • Developed the Kinase drUgs mAchine Learning frAmework (KUALA).
  • Implemented algorithms to generate multi-target priority scores and repurposing thresholds.

Main Results:

  • Successfully identified kinase-active ligands using the KUALA framework.
  • Generated quantitative scores to prioritize multi-target drug effects.
  • Established a threshold for suggesting repurposable and non-repurposable drug candidates.
  • A comprehensive list of kinase-ligand pairs and their scores is publicly available.

Conclusions:

  • The KUALA framework offers an effective computational approach for kinase-ligand identification and drug repurposing.
  • The developed scoring system aids in understanding poly-pharmacology and optimizing drug development strategies.
  • This work provides valuable resources for accelerating the discovery of new kinase-targeted therapies.