In this work, the phosphatidylcholine membrane-water partition coefcients (MA) of some drugs were estimated from their theoretical derived molecular descriptors by applying quantitative structure-activity relationship (QSAR) methodology. The data set consisted of 46 drugs where their log MA were determined experimentally. Descriptors used in this work were calculated by DRAGON (version 1) package, on the basis of optimized molecular structures, and the most relevant descriptors were selected by stepwise multilinear regressions (MLRs). These descriptors were used to developing linear and nonlinear models by using MLR and artifcial neural networks (ANNs), respectively. During this investigation, the best QSAR model was identifed when using the ANN model that produced a reasonable level of correlation coefcients (R train = 0.995, Rtest = 0.948) and low standard error (SEtrain = 0.099, SEtest = 0.326). The built model was fully assessed by various validation methods, including internal and external validation test, Y-randomization test, and cross-validation (Q2 = 0.805). The results of this investigation revealed the applicability of QSAR approaches in the estimation of phosphatidylcholine membrane-water partition coefcients.
