Comparison of Two Methods Forecasting Binding Rate of Plasma Protein
Author(s) -
Hongjiu Liu,
Yanrong Hu
Publication year - 2014
Publication title -
computational and mathematical methods in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.462
H-Index - 48
eISSN - 1748-6718
pISSN - 1748-670X
DOI - 10.1155/2014/957154
Subject(s) - support vector machine , computer science , artificial intelligence , algorithm , robustness (evolution) , machine learning , chemistry , biochemistry , gene
By introducing the descriptors calculated from the molecular structure, the binding rates of plasma protein (BRPP) with seventy diverse drugs are modeled by a quantitative structure-activity relationship (QSAR) technique. Two algorithms, heuristic algorithm (HA) and support vector machine (SVM), are used to establish linear and nonlinear models to forecast BRPP. Empirical analysis shows that there are good performances for HA and SVM with cross-validation correlation coefficients R cv 2 of 0.80 and 0.83. Comparing HA with SVM, it was found that SVM has more stability and more robustness to forecast BRPP.
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