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The prediction of the excitation and the emission maxima of green fluorescent protein (GFP) chromophores were investigated by a quantitative structure-property relationship study. A data set of 19 GFP color variants and an additional data set consisting of 29 synthetic GFP chromophores were collected from the literature. Artificial neural network implementing the back-propagation algorithm was employed. The proposed computational approach reliably predicted the excitation and the emission maxima of GFP chromophores with correlation coefficient exceeding 0.9. The usefulness of quantum chemical descriptors was revealed by a comparative study with other molecular descriptors. Assignment of appropriate protonation state of the chromophore for the GFP color variants data set was shown to be necessary for good predictive performance. Results suggest that the confinement of the GFP chromophore has no significant influence on the predictive performance of the data set used. A comparative investigation with the traditional modeling methods, particularly multiple linear regression and partial least squares, reveals that artificial neural network is the most suitable modeling approach for the GFP spectral properties. It is anticipated that this methodology has great potential in accelerating the design and engineering of novel GFP color variants of scientific or industrial interest.

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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.