Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester
3β-O-phthalic ester of betulinic acid was synthesized from reaction of betulinic acid and phthalic anhydride using lipase as biocatalyst. This ester has clinical potential as an anticancer agent. In this study, artificial neural network (ANN) analysis of Candida antarctica lipase (Novozym 435) -cata...
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ftunivtoronto:oai:localhost:1807/49063 2023-05-15T13:32:59+02:00 Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester Moghaddam, Mansour Ghaffari Ahmad, Faujan Bin H. Basri, Mahiran Rahman, Mohd Basyaruddin Abdul 2011-03-29 http://hdl.handle.net/1807/49063 http://www.bioline.org.br/abstract?id=ej10025 http://www.bioline.org.br/ej http://www.ejbiotechnology.info en eng Universidad Católica de Valparaíso http://hdl.handle.net/1807/49063 http://www.bioline.org.br/abstract?id=ej10025 http://www.bioline.org.br/ej http://www.ejbiotechnology.info Copyright 2010 - Pontificia Universidad Católica de Valparaíso -- Chile Novozym 435 enzymatic synthesis Candida antarctica lipase betulinic acid artificial neural network acylation Article 2011 ftunivtoronto 2020-06-17T11:33:00Z 3β-O-phthalic ester of betulinic acid was synthesized from reaction of betulinic acid and phthalic anhydride using lipase as biocatalyst. This ester has clinical potential as an anticancer agent. In this study, artificial neural network (ANN) analysis of Candida antarctica lipase (Novozym 435) -catalyzed esterification of betulinic acid with phthalic anhydride was carried out. A multilayer feed-forward neural network trained with an error back-propagation algorithm was incorporated for developing a predictive model. The input parameters of the model are reaction time, reaction temperature, enzyme amount and substrate molar ratio while the percentage isolated yield of ester is the output. Four different training algorithms, belonging to two classes, namely gradient descent and Levenberg-Marquardt (LM), were used to train ANN. The paper makes a robust comparison of the performances of the above four algorithms employing standard statistical indices. The results showed that the quick propagation algorithm (QP) with 4-9-1 arrangement gave the best performances. The root mean squared error (RMSE), coefficient of determination (R2) and absolute average deviation (AAD) between the actual and predicted yields were determined as 0.0335, 0.9999 and 0.0647 for training set, 0.6279, 0.9961 and 1.4478 for testing set and 0.6626, 0.9488 and 1.0205 for validation set using quick propagation algorithm (QP). Article in Journal/Newspaper Antarc* Antarctica University of Toronto: Research Repository T-Space |
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Open Polar |
collection |
University of Toronto: Research Repository T-Space |
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ftunivtoronto |
language |
English |
topic |
Novozym 435 enzymatic synthesis Candida antarctica lipase betulinic acid artificial neural network acylation |
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Novozym 435 enzymatic synthesis Candida antarctica lipase betulinic acid artificial neural network acylation Moghaddam, Mansour Ghaffari Ahmad, Faujan Bin H. Basri, Mahiran Rahman, Mohd Basyaruddin Abdul Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester |
topic_facet |
Novozym 435 enzymatic synthesis Candida antarctica lipase betulinic acid artificial neural network acylation |
description |
3β-O-phthalic ester of betulinic acid was synthesized from reaction of betulinic acid and phthalic anhydride using lipase as biocatalyst. This ester has clinical potential as an anticancer agent. In this study, artificial neural network (ANN) analysis of Candida antarctica lipase (Novozym 435) -catalyzed esterification of betulinic acid with phthalic anhydride was carried out. A multilayer feed-forward neural network trained with an error back-propagation algorithm was incorporated for developing a predictive model. The input parameters of the model are reaction time, reaction temperature, enzyme amount and substrate molar ratio while the percentage isolated yield of ester is the output. Four different training algorithms, belonging to two classes, namely gradient descent and Levenberg-Marquardt (LM), were used to train ANN. The paper makes a robust comparison of the performances of the above four algorithms employing standard statistical indices. The results showed that the quick propagation algorithm (QP) with 4-9-1 arrangement gave the best performances. The root mean squared error (RMSE), coefficient of determination (R2) and absolute average deviation (AAD) between the actual and predicted yields were determined as 0.0335, 0.9999 and 0.0647 for training set, 0.6279, 0.9961 and 1.4478 for testing set and 0.6626, 0.9488 and 1.0205 for validation set using quick propagation algorithm (QP). |
format |
Article in Journal/Newspaper |
author |
Moghaddam, Mansour Ghaffari Ahmad, Faujan Bin H. Basri, Mahiran Rahman, Mohd Basyaruddin Abdul |
author_facet |
Moghaddam, Mansour Ghaffari Ahmad, Faujan Bin H. Basri, Mahiran Rahman, Mohd Basyaruddin Abdul |
author_sort |
Moghaddam, Mansour Ghaffari |
title |
Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester |
title_short |
Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester |
title_full |
Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester |
title_fullStr |
Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester |
title_full_unstemmed |
Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester |
title_sort |
artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester |
publisher |
Universidad Católica de Valparaíso |
publishDate |
2011 |
url |
http://hdl.handle.net/1807/49063 http://www.bioline.org.br/abstract?id=ej10025 http://www.bioline.org.br/ej http://www.ejbiotechnology.info |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_relation |
http://hdl.handle.net/1807/49063 http://www.bioline.org.br/abstract?id=ej10025 http://www.bioline.org.br/ej http://www.ejbiotechnology.info |
op_rights |
Copyright 2010 - Pontificia Universidad Católica de Valparaíso -- Chile |
_version_ |
1766037440172130304 |