QSPR Study of Fluorescence Wavelengths (λex/λem) Based on the Heuristic Method and Radial Basis Function Neural Networks
ABSTRACT The Quantitative Structure–Property Relationship (QSPR) method was performed to study the fluorescence excitation wavelengths (λex) and emission wavelengths (λem) of 64 fluorescent probes. The probes included the derivatives of dansyl, bimane, pyrene, benzofurazan, nonaphthalene, coumarin, anthracene and fluorescein, with the wavelength ranging from 300 nm to 600 nm. The Heuristic Method (HM) and Radial Basis Function Neural Networks (RBFNNs) were employed to construct linear and nonlinear prediction models, respectively. The proposed linear models for λex and λem contain five descriptors with the squared correlation coefficients R2 of 0.888 and 0.897, respectively. Better prediction results were obtained from RBFNN model, with the squared correlation coefficients R2 of 0.948 and 0.939 for λex and λem, respectively. The descriptors used in the models were discussed in detail too.
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ABSTRACT: This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared on the QASR/QSPR stage. At the same time, the earlier methods, including Multiple Linear Regression (MLR), Partial Least Squares (PLS), Neural Networks (NN), Support Vector Machine (SVM) and so on, are being upgraded to improve their performance in QASR/QSPR studies. These new and upgraded methods and algorithms are described in detail, and their advantages and disadvantages are evaluated and discussed, to show their application potential in QASR/QSPR studies in the future.International Journal of Molecular Sciences 06/2009; 10(5):1978-98. · 2.46 Impact Factor
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ABSTRACT: Models to predict binding constant (logK) to bovine serum albumin (BSA) should be very useful in the pharmaceutical industry to help speed up the design of new compounds, especially as far as pharmacokinetics is concerned. We present here an extensive list of logK binding constants for thirty-five compounds to BSA determined by florescence quenching from the literature. These data have allowed us the derivation of a quantitative structure-property relationship (QSPR) model to predict binding constants to BSA of compounds on the basis of their structure. A stepwise multiple linear regression (MLR) was performed to build the model. The statistical parameter provided by the MLR model (R = 0.9200, RMS = 0.3305) indicated satisfactory stability and predictive ability for the model. Using florescence quenching spectroscopy, we also experimentally determined the binding constants to BSA for two bioactive components in traditional Chinese medicines. Using the proposed model it was possible to predict the binding constants for each, which were in good agreement with the experimental results. This QSPR approach can contribute to a better understanding of structural factors of the compounds responsible for drug-protein interactions, and be useful in predicting the binding constants of other compounds.Central European Journal of Chemistry 01/2009; 7(1):59-65. · 1.17 Impact Factor
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ABSTRACT: Quantitative structure-retention relationship (QSRR) models were used to predict the retention time (t(R)) of mycotoxins and fungal metabolites. Heuristic method and radial basis function neural networks (RBFNN) were utilized to construct the linear and non-linear QSRR models, respectively. The optimal QSRR model was developed based on a 5-21-1 RBFNN architecture using molecular descriptors calculated from molecular structure alone. The RBFNN model gave a square of correlation coefficient (R(2)) of 0.8709 and root mean square error of 1.2892 for the test set. This article provided a useful tool for predicting the t(R) of other mycotoxins when experiment data are unknown.Journal of Separation Science 10/2009; 32(22):3967-79. · 2.59 Impact Factor