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

One of the recently developed metaheuristic algorithms, the coyote optimization algorithm (COA), has shown to perform better in a number of difficult optimization tasks. The binary form, BCOA, is used in this study as a solution to the descriptor selection issue in classifying diverse antifungal ser...

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Vydáno v:SAR and QSAR in environmental research Ročník 34; číslo 4; s. 285 - 298
Hlavní autoři: Al-Fakih, A.M., Qasim, M.K., Algamal, Z.Y., Alharthi, A.M., Zainal-Abidin, M.H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Taylor & Francis 03.04.2023
Taylor & Francis Ltd
Témata:
Algorithms
Animals
Antifungal Agents
antimicrobial
Area Under Curve
binary coyote optimization algorithm
Candida albicans
Classification
Coyotes
descriptor selection
Fungicides
Geometric accuracy
Heuristic methods
Optimization
Optimization algorithms
Performance enhancement
Quantitative Structure-Activity Relationship
Structure-activity relationships
Transfer functions
Z-shape transfer functions
Candida albicans
Z-shape transfer functions
antimicrobial
descriptor selection
binary coyote optimization algorithm
ISSN:1062-936X, 1029-046X, 1029-046X
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Shrnutí:One of the recently developed metaheuristic algorithms, the coyote optimization algorithm (COA), has shown to perform better in a number of difficult optimization tasks. The binary form, BCOA, is used in this study as a solution to the descriptor selection issue in classifying diverse antifungal series. Z-shape transfer functions (ZTF) are evaluated to verify their efficiency in improving BCOA performance in QSAR classification based on classification accuracy (CA), the geometric mean of sensitivity and specificity (G-mean), and the area under the curve (AUC). The Kruskal-Wallis test is also applied to show the statistical differences between the functions. The efficacy of the best suggested transfer function, ZTF4, is further assessed by comparing it to the most recent binary algorithms. The results prove that ZTF, especially ZTF4, significantly improves the performance of the original BCOA. The ZTF4 function yields the best CA and G-mean of 99.03% and 0.992%, respectively. It shows the fastest convergence behaviour compared to other binary algorithms. It takes the fewest iterations to reach high classification performance and selects the fewest descriptors. In conclusion, the obtained results indicate the ability of the ZTF4-based BCOA to find the smallest subset of descriptors while maintaining the best classification accuracy performance.
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ISSN:1062-936X
1029-046X
1029-046X
DOI:10.1080/1062936X.2023.2208374