Publication: Quantitative prediction of imprinting factor of molecularly imprinted polymers by artificial neural network
Issued Date
2005-07-01
Resource Type
ISSN
15734951
0920654X
0920654X
Other identifier(s)
2-s2.0-31444441573
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Mahidol University
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SCOPUS
Bibliographic Citation
Journal of Computer-Aided Molecular Design. Vol.19, No.7 (2005), 509-524
Suggested Citation
Chanin Nantasenamat, Thanakorn Naenna, Chartchalerm Isarankura Na-Ayudhya, Virapong Prachayasittikul Quantitative prediction of imprinting factor of molecularly imprinted polymers by artificial neural network. Journal of Computer-Aided Molecular Design. Vol.19, No.7 (2005), 509-524. doi:10.1007/s10822-005-9004-4 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/16439
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Title
Quantitative prediction of imprinting factor of molecularly imprinted polymers by artificial neural network
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Abstract
Artificial neural network (ANN) implementing the back-propagation algorithm was applied for the calculation of the imprinting factors (IF) of molecularly imprinted polymers (MIP) as a function of the computed molecular descriptors of template and functional monomer molecules and mobile phase descriptors. The dataset used in our study were obtained from the literature and classified into two distinctive datasets on the basis of the polymer's morphology, irregularly sized MIP and uniformly sized MIP datasets. Results revealed that artificial neural network was able to perform well on datasets derived from uniformly sized MIP (n=23, r=0.946, RMS=2.944) while performing poorly on datasets derived from irregularly sized MIP (n=75, r=0.382, RMS=6.123). The superior performance of the uniformly sized MIP dataset over the irregularly sized MIP dataset could be attributed to its more predictable nature owing to the consistency of MIP particles, uniform number and association constant of binding sites, and minimal deviation of the imprinted polymers. The ability to predict the imprinting factor of imprinted polymer prior to performing actual experimental work provide great insights on the feasibility of the interaction between template-functional monomer pairs. © Springer Science+Business Media, Inc. 2005.