Universiti Teknologi Malaysia Institutional Repository

Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression

Alharthi, A. M. and Lee, M. H. and Algamal, Z. Y. and Al-Fakih, A. M. (2020) Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression. SAR and QSAR in Environmental Research . pp. 571-583. ISSN 1062-936X

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Official URL: https://doi.org/10.1080/1062936X.2020.1782467

Abstract

One of the most challenging issues when facing a Quantitative structure-activity relationship (QSAR) classification model is to deal with the descriptor selection. Penalized methods have been adapted and have gained popularity as a key for simultaneously performing descriptor selection and QSAR classification model estimation. However, penalized methods have drawbacks such as having biases and inconsistencies that make they lack the oracle properties. This paper proposes an adaptive penalized logistic regression (APLR) to overcome these drawbacks. This is done by employing a ratio (BWR) of the descriptors between-groups sum of squares (BSS) to the within-groups sum of squares (WSS) for each descriptor as a weight inside the L1-norm. The proposed method was applied to one dataset that consists of a diverse series of antimicrobial agents with their respective bioactivities against Candida albicans. By experimental study, it has been shown that the proposed method (APLR) was more efficient in the selection of descriptors and classification accuracy than the other competitive methods that could be used in developing QSAR classification models. Another dataset was also successfully experienced. Therefore, it can be concluded that the APLR method had significant impact on QSAR analysis and studies.

Item Type:Article
Uncontrolled Keywords:antifungal agents, descriptor selection, penalized logistic model
Subjects:Q Science > QA Mathematics
Divisions:Science
ID Code:93839
Deposited By: Narimah Nawil
Deposited On:31 Jan 2022 08:36
Last Modified:31 Jan 2022 08:36

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