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 (KBSA/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 KBSA/W. The correlation coefficients, R, between experimental and predicted log KBSA/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.