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Qsar classification model for diverse series of antifungal agents based on improved binary differential search algorithm

Al-Fakih, A. M. and Algamal, Z. Y. and Lee, M. H. and Aziz, M. and Ali, H. T. M. (2019) Qsar classification model for diverse series of antifungal agents based on improved binary differential search algorithm. SAR and QSAR in Environmental Research, 30 (2). pp. 131-143. ISSN 1062-936X

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Official URL: http://dx.doi.org/10.1080/1062936X.2019.1568298

Abstract

An improved binary differential search (improved BDS) algorithm is proposed for QSAR classification of diverse series of antimicrobial compounds against Candida albicans inhibitors. The transfer functions is the most important component of the BDS algorithm, and converts continuous values of the donor into discrete values. In this paper, the eight types of transfer functions are investigated to verify their efficiency in improving BDS algorithm performance in QSAR classification. The performance was evaluated using three metrics: classification accuracy (CA), geometric mean of sensitivity and specificity (G-mean), and area under the curve. The Kruskal–Wallis test was also applied to show the statistical differences between the functions. Two functions, S1 and V4, show the best classification achievement, with a slightly better performance of V4 than S1. The V4 function takes the lowest iterations and selects the fewest descriptors. In addition, the V4 function yields the best CA and G-mean of 98.07% and 0.977%, respectively. The results prove that the V4 transfer function significantly improves the performance of the original BDS.

Item Type:Article
Uncontrolled Keywords:S-shaped transfer functions, transfer function, V-shaped transfer functions
Subjects:Q Science > QD Chemistry
Divisions:Science
ID Code:89451
Deposited By: Yanti Mohd Shah
Deposited On:22 Feb 2021 14:04
Last Modified:22 Feb 2021 14:04

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