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

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.

KEYWORDS:

• Transfer function
• BDS algorithm
• Candida albicans
• S-shaped transfer functions
• V-shaped transfer functions

Supplemental Material

Supplementary material for this article can be accessed here: https://doi.org/10.1080/1062936X.2019.1568298

Acknowledgements

The authors acknowledge the Ministry of Education Malaysia through the Fundamental Research Grant Scheme and the Universiti Teknologi Malaysia through the Professional Development Research University Grant (grant number Q.J130000.21A2.04E48) for their funding of this research.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Universiti Teknologi Malaysia [Q.J130000.21A2.04E48].