Summary
Quantitative structure-property relationship (QSPR) method was performed for the prediction of the standard Gibbs energies (ΔGθ) of the transfer of peptide anions from aqueous solution to nitrobenzene. Descriptors calculated from the molecular structures alone were used to represent the characteristics of the peptides. The four molecular descriptors selected by the heuristic method (HM) in COmprehensive DEscriptors for Structural and Statistical Analysis (CODESSA) were used as inputs for support vector machine (SVM) and radial basis function neural networks (RNFNN). The results obtained by the novel machine learning technique, SVM, were compared with those obtained by HM and RBFNN. The root mean squared errors (RMS) of the training, predicted and overall data sets are 2.192, 2.541 and 2.267 unit (kJ/mol) for HM, 1.604, 2.478 and 1.817 unit (kJ/mol) for RBFNN and 1.5621, 2.364 and 1.756 unit (kJ/mol) for SVM, respectively. The prediction results were in agreement with the experimental values. This paper provided a potential method for predicting the physiochemical property (ΔGθ) of various small peptides.
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Acknowledgements
The authors thank the National Natural Science Foundation of China (NSFC) Fund (NO.20305008) for financial support. The authors also thank the Association Franco-Chinoise pour la Recherche Scientifique & Technique (AFCRST) for supporting this study (Programme PRA SI 02–03).
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Feng, L., Xiaoyun, Z., Haixia, Z. et al. Prediction of standard Gibbs energies of the transfer of peptide anions from aqueous solution to nitrobenzene based on support vector machine and the heuristic method. J Comput Aided Mol Des 20, 1–11 (2006). https://doi.org/10.1007/s10822-005-9031-1
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DOI: https://doi.org/10.1007/s10822-005-9031-1