caa a22 4500 467938547 CHVBK 20180406153005.0 cr unu---uuuuu 170328e20060101xx s 000 0 eng 10.1007/s10822-005-9031-1 doi (NATIONALLICENCE)springer-10.1007/s10822-005-9031-1 Prediction of standard Gibbs energies of the transfer of peptide anionsfrom aqueous solution to nitrobenzene based on support vector machine and the heuristic method [Elektronische Daten] [Luan Feng, Zhang Xiaoyun, Zhang Haixia, Zhang Ruisheng, Liu Mancang, Hu Zhide, Fan Botao] 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. Springer Science+Business Media, Inc., 2006 HM nationallicence QSPR nationallicence peptide nationallicence SVM nationallicence ΔGθ nationallicence Feng Luan Department of Chemistry, Lanzhou University, 730000, Lanzhou, China aut Xiaoyun Zhang Department of Chemistry, Lanzhou University, 730000, Lanzhou, China aut Haixia Zhang Department of Chemistry, Lanzhou University, 730000, Lanzhou, China aut Ruisheng Zhang Department of Computer Science, Lanzhou University, 730000, Lanzhou, China aut Mancang Liu Department of Chemistry, Lanzhou University, 730000, Lanzhou, China aut Zhide Hu Department of Chemistry, Lanzhou University, 730000, Lanzhou, China aut Botao Fan Université Paris 7-Denis Diderot, ITODYS 1, Rue Guy de la Brosse, 75005, Paris, France aut Journal of Computer-Aided Molecular Design Springer Netherlands 20/1(2006-01-01), 1-11 0920-654X 20:1<1 2006 20 10822 https://doi.org/10.1007/s10822-005-9031-1 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10822-005-9031-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Feng Luan Department of Chemistry, Lanzhou University, 730000, Lanzhou, China aut NATIONALLICENCE 700 1- Xiaoyun Zhang Department of Chemistry, Lanzhou University, 730000, Lanzhou, China aut NATIONALLICENCE 700 1- Haixia Zhang Department of Chemistry, Lanzhou University, 730000, Lanzhou, China aut NATIONALLICENCE 700 1- Ruisheng Zhang Department of Computer Science, Lanzhou University, 730000, Lanzhou, China aut NATIONALLICENCE 700 1- Mancang Liu Department of Chemistry, Lanzhou University, 730000, Lanzhou, China aut NATIONALLICENCE 700 1- Zhide Hu Department of Chemistry, Lanzhou University, 730000, Lanzhou, China aut NATIONALLICENCE 700 1- Botao Fan Université Paris 7-Denis Diderot, ITODYS 1, Rue Guy de la Brosse, 75005, Paris, France aut NATIONALLICENCE 773 0- Journal of Computer-Aided Molecular Design Springer Netherlands 20/1(2006-01-01), 1-11 0920-654X 20:1<1 2006 20 10822 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer