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Quantitative prediction of imprinting factor of molecularly imprinted polymers by artificial neural network

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Summary

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.

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Abbreviations

MIP:

molecularly imprinted polymer

NIP:

non-imprinted polymer

IF:

imprinting factor

RMS:

root mean square error

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Acknowledgements

C.N. is grateful for the Royal Golden Jubilee Ph.D. Scholarship under V.P. supervision from The Thailand Research Fund. This project was also partially supported by the Thailand Toray Science Foundation (TTSF) and a grant from the annual budget of Mahidol University (B.E.2548).

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Authors and Affiliations

  1. Department of Clinical Microbiology, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand

    Chanin Nantasenamat, Chartchalerm Isarankura Na Ayudhya & Virapong Prachayasittikul

  2. Department of Industrial Engineering, Faculty of Engineering, Mahidol University, 73170, Nakhon Pathom, Thailand

    Thanakorn Naenna

Authors
  1. Chanin Nantasenamat
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  2. Thanakorn Naenna
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  3. Chartchalerm Isarankura Na Ayudhya
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  4. Virapong Prachayasittikul
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Correspondence to Virapong Prachayasittikul.

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Nantasenamat, C., Naenna, T., Ayudhya, C.I.N. et al. Quantitative prediction of imprinting factor of molecularly imprinted polymers by artificial neural network. J Comput Aided Mol Des 19, 509–524 (2005). https://doi.org/10.1007/s10822-005-9004-4

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  • DOI: https://doi.org/10.1007/s10822-005-9004-4

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