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QSAR classification model for diverse series of antifungal agents based on improved binary differential search
A M Al-Fakih1,2, Z Y Algamal3, M H Lee4
1a Department of Chemistry , Universiti Teknologi Malaysia , Johor , Malaysia.
SAR and QSAR in Environmental Research
|February 9, 2019
Summary
An improved binary differential search algorithm enhances quantitative structure-activity relationship (QSAR) classification for antimicrobial compounds. The V4 transfer function achieved the best results, improving accuracy and reducing computational complexity.
Area of Science:
- Computational chemistry
- Medicinal chemistry
- Bioinformatics
Background:
- Quantitative structure-activity relationship (QSAR) models are crucial for drug discovery.
- The binary differential search (BDS) algorithm is a metaheuristic optimization technique used in QSAR.
- Improving the efficiency and accuracy of QSAR classification is essential for identifying novel antimicrobial agents.
Purpose of the Study:
- To propose an improved binary differential search (improved BDS) algorithm for QSAR classification.
- To investigate the impact of different transfer functions on the performance of the BDS algorithm.
- To identify the optimal transfer function for QSAR classification of Candida albicans inhibitors.
Main Methods:
- Eight types of transfer functions were evaluated within the improved BDS algorithm.
- Performance metrics included classification accuracy (CA), G-mean, and area under the curve.
- Statistical significance of differences between functions was assessed using the Kruskal-Wallis test.
Main Results:
- The V4 transfer function demonstrated superior performance compared to other functions, including S1.
- The V4 function achieved the highest classification accuracy (98.07%) and G-mean (0.977%).
- The V4 function required the fewest iterations and selected the minimum number of descriptors.
Conclusions:
- The V4 transfer function significantly enhances the performance of the original BDS algorithm for QSAR classification.
- The improved BDS algorithm with the V4 function is effective for identifying antimicrobial compounds against Candida albicans.
- This study provides a robust method for optimizing QSAR models in drug discovery.
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