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

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 clas...

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Main Authors: Alharthi, A. M., Lee, M. H., Algamal, Z. Y., Al-Fakih, A. M.
Format: Article
Published: Taylor and Francis Ltd. 2020
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Online Access:http://eprints.utm.my/id/eprint/93839/
https://doi.org/10.1080/1062936X.2020.1782467
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Institution: Universiti Teknologi Malaysia
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spelling my.utm.938392022-01-31T08:36:46Z http://eprints.utm.my/id/eprint/93839/ Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression Alharthi, A. M. Lee, M. H. Algamal, Z. Y. Al-Fakih, A. M. QA Mathematics 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. Taylor and Francis Ltd. 2020 Article PeerReviewed 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 https://doi.org/10.1080/1062936X.2020.1782467 DOI: 10.1080/1062936X.2020.1782467
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA Mathematics
spellingShingle QA Mathematics
Alharthi, A. M.
Lee, M. H.
Algamal, Z. Y.
Al-Fakih, A. M.
Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression
description 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.
format Article
author Alharthi, A. M.
Lee, M. H.
Algamal, Z. Y.
Al-Fakih, A. M.
author_facet Alharthi, A. M.
Lee, M. H.
Algamal, Z. Y.
Al-Fakih, A. M.
author_sort Alharthi, A. M.
title Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression
title_short Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression
title_full Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression
title_fullStr Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression
title_full_unstemmed Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression
title_sort quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression
publisher Taylor and Francis Ltd.
publishDate 2020
url http://eprints.utm.my/id/eprint/93839/
https://doi.org/10.1080/1062936X.2020.1782467
_version_ 1724073272576835584