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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my.utm.1067292024-07-28T06:09:59Z http://eprints.utm.my/106729/ QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm Mohammed Al-Fakih, Abdo Khalid Qasim, Maimoonah Algamal, Zakariya Y. Alharthi, Aiedh Mrisi Zainal Abidin, Mohamad Hamdi QD Chemistry 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. Taylor & Francis Group 2023 Article PeerReviewed Mohammed Al-Fakih, Abdo and Khalid Qasim, Maimoonah and Algamal, Zakariya Y. and Alharthi, Aiedh Mrisi and Zainal Abidin, Mohamad Hamdi (2023) QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm. SAR and QSAR in Environmental Research, 34 (4). pp. 285-298. ISSN 1062-936X http://dx.doi.org/10.1080/1062936X.2023.2208374 DOI:10.1080/1062936X.2023.2208374 |
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QD Chemistry Mohammed Al-Fakih, Abdo Khalid Qasim, Maimoonah Algamal, Zakariya Y. Alharthi, Aiedh Mrisi Zainal Abidin, Mohamad Hamdi QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm |
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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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Article |
author |
Mohammed Al-Fakih, Abdo Khalid Qasim, Maimoonah Algamal, Zakariya Y. Alharthi, Aiedh Mrisi Zainal Abidin, Mohamad Hamdi |
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Mohammed Al-Fakih, Abdo Khalid Qasim, Maimoonah Algamal, Zakariya Y. Alharthi, Aiedh Mrisi Zainal Abidin, Mohamad Hamdi |
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Mohammed Al-Fakih, Abdo |
title |
QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm |
title_short |
QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm |
title_full |
QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm |
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QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm |
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QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm |
title_sort |
qsar classification model for diverse series of antifungal agents based on binary coyote optimization algorithm |
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Taylor & Francis Group |
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2023 |
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http://eprints.utm.my/106729/ http://dx.doi.org/10.1080/1062936X.2023.2208374 |
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