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Prediction of Bovine Serum Albumin‐Water Partition Coefficients of a Wide Variety of Neutral Organic Compounds by Means of Support Vector Machine
Author(s) -
Golmohammadi Hassan,
Dashtbozorgi Zahra,
Acree William E.
Publication year - 2012
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201200091
Subject(s) - quantitative structure–activity relationship , support vector machine , partition coefficient , molecular descriptor , artificial neural network , biological system , linear regression , bovine serum albumin , chemistry , cross validation , predictability , artificial intelligence , mathematics , correlation coefficient , pattern recognition (psychology) , computer science , statistics , chromatography , stereochemistry , biology
Support vector machine (SVM) was used to develop a quantitative structure property relationship (QSPR) model that correlates molecular structures to their bovine serum albumin water partition coefficients ( K BSA/W ). The performance and predictive aptitude of SVM are considered and compared with other methods such as multiple linear regression (MLR) and artificial neural network (ANN) methods. A set of 83 natural organic compounds and drugs were selected and suitable sets of molecular descriptors were calculated. Genetic algorithm (GA) was used to select important molecular descriptors, and linear and nonlinear models were applied to correlate the selected descriptors with the experimental values of log K BSA/W . The correlation coefficients, R , between experimental and predicted log K BSA/W for the validation set by MLR, ANN and SVM are 0.951, 0.986 and 0.991, respectively. Results obtained document the reliability and good predictability of the nonlinear QSPR model to predict partition coefficients of organic compounds. Comparison between the values of statistical parameters demonstrates that the predictive ability of the SVM model is comparable or superior to those obtained by MLR and ANN.