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QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm.

A M Al-Fakih1,2, M K Qasim3, Z Y Algamal4,5

  • 1Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, Johor, Malaysia.

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Summary

This study enhances the binary coyote optimization algorithm (BCOA) for quantitative structure-activity relationship (QSAR) classification using Z-shape transfer functions (ZTF). ZTF4 significantly improves BCOA performance, achieving high accuracy and selecting minimal descriptors.

Keywords:
Candida albicansZ-shape transfer functionsantimicrobialbinary coyote optimization algorithmdescriptor selection

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Area of Science:

  • Computational Chemistry
  • Bioinformatics
  • Machine Learning

Background:

  • Metaheuristic algorithms offer robust solutions for complex optimization problems.
  • The coyote optimization algorithm (COA) is a novel metaheuristic with demonstrated effectiveness.
  • Descriptor selection is crucial for building accurate quantitative structure-activity relationship (QSAR) models.

Purpose of the Study:

  • To evaluate the efficiency of Z-shape transfer functions (ZTF) in enhancing the binary coyote optimization algorithm (BCOA) for QSAR classification.
  • To identify the optimal ZTF for improving BCOA performance in classifying antifungal compounds.
  • To compare the performance of the enhanced BCOA against existing binary algorithms.

Main Methods:

  • Binary coyote optimization algorithm (BCOA) was employed for descriptor selection in QSAR classification.
  • Various Z-shape transfer functions (ZTF) were integrated with BCOA to assess their impact.
  • Performance metrics included classification accuracy (CA), geometric mean (G-mean), and area under the curve (AUC).
  • Statistical significance was analyzed using the Kruskal-Wallis test.

Main Results:

  • The ZTF, particularly ZTF4, significantly boosted BCOA performance in QSAR classification.
  • ZTF4 achieved the highest classification accuracy (99.03%) and G-mean (0.992).
  • The ZTF4-based BCOA demonstrated faster convergence and required fewer iterations and descriptors than other algorithms.

Conclusions:

  • Z-shape transfer functions, especially ZTF4, effectively enhance the BCOA for descriptor selection in QSAR.
  • The ZTF4-BCOA approach successfully identifies a minimal set of descriptors while maintaining superior classification accuracy.
  • This method offers a promising strategy for developing accurate and efficient QSAR models.