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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Bibliographic Details
Published in:	SAR and QSAR in environmental research Vol. 34; no. 4; pp. 285 - 298
Main Authors:	Al-Fakih, A.M., Qasim, M.K., Algamal, Z.Y., Alharthi, A.M., Zainal-Abidin, M.H.
Format:	Journal Article
Language:	English
Published:	England Taylor & Francis 03.04.2023 Taylor & Francis Ltd
Subjects:	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
Online Access:	Get full text
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