Corrosion inhibition efficiency of thiophene derivatives on mild steel: a QSAR model

A quantitative structure activity relationship (QSAR) model was built using Interval Partial Least Squares and Partial Least Squares (IPLS-PLS) regression for the prediction of corrosion inhibition efficiency of thiophene derivatives. Eleven compounds with their activity expressed as percentage inhi...

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Main Authors: Usman, Bishir, Maarof, Hasmerya, Abdallah, Hassan Hadi, Jamaludin, Rosmahaida, Al-Fakih, Abdo Mohammed, Aziz, Madzlan
格式: Article
語言:English
出版: ESG. 2014
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在線閱讀:http://eprints.utm.my/id/eprint/52244/1/BishirUsman2014_Corrosioninhibitionefficiency.pdf
http://eprints.utm.my/id/eprint/52244/
http://www.electrochemsci.org/papers/vol9/90401678.pdf
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spelling my.utm.522442018-09-17T04:01:23Z http://eprints.utm.my/id/eprint/52244/ Corrosion inhibition efficiency of thiophene derivatives on mild steel: a QSAR model Usman, Bishir Maarof, Hasmerya Abdallah, Hassan Hadi Jamaludin, Rosmahaida Al-Fakih, Abdo Mohammed Aziz, Madzlan Q Science A quantitative structure activity relationship (QSAR) model was built using Interval Partial Least Squares and Partial Least Squares (IPLS-PLS) regression for the prediction of corrosion inhibition efficiency of thiophene derivatives. Eleven compounds with their activity expressed as percentage inhibition efficiency (%IE) were obtained and divided into a training set (ntrn = 7) and test set (ntes= 4). Molecular descriptors were generated using Dragon software and the important relevant descriptors were selected using an objective variable selection followed by subjective variable selection using IPLS. Several models were built using PLS regression and the models were evaluated using statistical significance characterization, r2 and root mean square error calibration (RMSEC). The robustness, accuracy and predictive ability of the models were carried out using external and internal cross validation using regression coefficient cross validation (r2 cv) and regression coefficient prediction (r2 pred). The values were calculated and found to be > 0.5 and 0.8 respectively for the first and second model and for the external validation the values are found to be > 0.6 and 0.5 respectively and the r2 value was found to be > 0.9. Application of the built model to calculate the theoretical %IE was obtained and is closer to the %IE experimental. The result showed the predictive ability of the model was good and can be used to design a similar group of compounds with corrosion inhibition efficiency ESG. 2014 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/52244/1/BishirUsman2014_Corrosioninhibitionefficiency.pdf Usman, Bishir and Maarof, Hasmerya and Abdallah, Hassan Hadi and Jamaludin, Rosmahaida and Al-Fakih, Abdo Mohammed and Aziz, Madzlan (2014) Corrosion inhibition efficiency of thiophene derivatives on mild steel: a QSAR model. International Journal of Electrochemical Science, 9 (4). pp. 1678-1689. ISSN 1452-3981 http://www.electrochemsci.org/papers/vol9/90401678.pdf
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/
language English
topic Q Science
spellingShingle Q Science
Usman, Bishir
Maarof, Hasmerya
Abdallah, Hassan Hadi
Jamaludin, Rosmahaida
Al-Fakih, Abdo Mohammed
Aziz, Madzlan
Corrosion inhibition efficiency of thiophene derivatives on mild steel: a QSAR model
description A quantitative structure activity relationship (QSAR) model was built using Interval Partial Least Squares and Partial Least Squares (IPLS-PLS) regression for the prediction of corrosion inhibition efficiency of thiophene derivatives. Eleven compounds with their activity expressed as percentage inhibition efficiency (%IE) were obtained and divided into a training set (ntrn = 7) and test set (ntes= 4). Molecular descriptors were generated using Dragon software and the important relevant descriptors were selected using an objective variable selection followed by subjective variable selection using IPLS. Several models were built using PLS regression and the models were evaluated using statistical significance characterization, r2 and root mean square error calibration (RMSEC). The robustness, accuracy and predictive ability of the models were carried out using external and internal cross validation using regression coefficient cross validation (r2 cv) and regression coefficient prediction (r2 pred). The values were calculated and found to be > 0.5 and 0.8 respectively for the first and second model and for the external validation the values are found to be > 0.6 and 0.5 respectively and the r2 value was found to be > 0.9. Application of the built model to calculate the theoretical %IE was obtained and is closer to the %IE experimental. The result showed the predictive ability of the model was good and can be used to design a similar group of compounds with corrosion inhibition efficiency
format Article
author Usman, Bishir
Maarof, Hasmerya
Abdallah, Hassan Hadi
Jamaludin, Rosmahaida
Al-Fakih, Abdo Mohammed
Aziz, Madzlan
author_facet Usman, Bishir
Maarof, Hasmerya
Abdallah, Hassan Hadi
Jamaludin, Rosmahaida
Al-Fakih, Abdo Mohammed
Aziz, Madzlan
author_sort Usman, Bishir
title Corrosion inhibition efficiency of thiophene derivatives on mild steel: a QSAR model
title_short Corrosion inhibition efficiency of thiophene derivatives on mild steel: a QSAR model
title_full Corrosion inhibition efficiency of thiophene derivatives on mild steel: a QSAR model
title_fullStr Corrosion inhibition efficiency of thiophene derivatives on mild steel: a QSAR model
title_full_unstemmed Corrosion inhibition efficiency of thiophene derivatives on mild steel: a QSAR model
title_sort corrosion inhibition efficiency of thiophene derivatives on mild steel: a qsar model
publisher ESG.
publishDate 2014
url http://eprints.utm.my/id/eprint/52244/1/BishirUsman2014_Corrosioninhibitionefficiency.pdf
http://eprints.utm.my/id/eprint/52244/
http://www.electrochemsci.org/papers/vol9/90401678.pdf
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score 13.250246