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Set-Theoretic Formalism for Treating Ligand-Target Datasets.

Gerald Maggiora1, Martin Vogt2

  • 1BIO5 Institute, University of Arizona, Tucson, AZ 85721, USA.

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|December 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new ternary set-theoretic approach to analyze sparse ligand-target interaction data, accounting for unknown interactions. This method enhances understanding of polypharmacology by including null pairs in drug discovery analysis.

Keywords:
ligand-target interactionspolypharmacologyset theoryternary relations

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Area of Science:

  • Computational chemistry and cheminformatics
  • Bioinformatics and systems biology
  • Drug discovery and development

Background:

  • Ligand-target (LT) interaction data is crucial in drug research but often suffers from extreme sparsity and lack of completeness.
  • Existing methods struggle to analyze datasets with numerous unknown LT pairs, hindering comprehensive analysis.

Purpose of the Study:

  • To develop a novel set-theoretic formalism for analyzing thresholded LT datasets, specifically addressing the challenge of unknown interactions.
  • To extend the concept of data completeness to individual ligands and targets (local data completeness).

Main Methods:

  • Development of a ternary set-theoretic formalism to handle LT pairs with three states: active, inactive, and unknown.
  • Extension of data completeness metrics to assess local data completeness for individual ligands and targets.
  • Application of the formalism to analyze simple and joint polypharmacologies using LT activity profiles.

Main Results:

  • The ternary set-theoretic approach effectively incorporates unknown LT pairs into dataset analysis.
  • Null pairs were shown to provide bounds for polypharmacology values.
  • The methodology was successfully applied to a dataset of protein kinase inhibitors.

Conclusions:

  • The developed formalism offers a robust method for analyzing sparse and incomplete LT interaction data in drug discovery.
  • The inclusion of unknown interactions and local data completeness provides a more accurate assessment of polypharmacology.
  • This approach has significant implications for improving the efficiency and accuracy of drug research.