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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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 |
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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. |
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Article |
author |
Alharthi, A. M. Lee, M. H. Algamal, Z. Y. Al-Fakih, A. M. |
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Alharthi, A. M. Lee, M. H. Algamal, Z. Y. Al-Fakih, A. M. |
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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 |
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Taylor and Francis Ltd. |
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2020 |
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http://eprints.utm.my/id/eprint/93839/ https://doi.org/10.1080/1062936X.2020.1782467 |
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