The descriptors that were reported to be most effective for LogBB prediction in earlier studies were selected for model building. Most of the earlier studies have shown that the Total Polar Surface Area (TPSA) is the best, or one of the best, descriptors for LogBB models. After experimenting with both linear regression models and PLS-regression, the following descriptors were utilized for PLS-regression models: Total Polar Surface Area (TPSA), Crippen's logP (logP), Molar refractivity (MR), Molecular weight (MW), Number of Aromatic rings (Naro) and Number of heavy atoms (Nhvy). Molecules with a calculated TPSA of zero were grouped separately from those that had a non-zero value of TPSA for prediction of LogBB. Two PLS-Regression models were built. One for molecules with non-zero value of TPSA, which constituted the majority of the compounds in the database, and a second for molecules with zero calculated TPSA. The set of descriptors and number of components were varied to optimize each of these PLS-regression models separately. PLS-regression analysis was implemented with 10-fold cross-validation for each model. PLS-regression models with TPSA, TPSA-squared and the square root of TPSA (TPSQRT) were evaluated for the subset that contained molecules with non-zero TPSA. The following PLSregression model was selected because it had the highest Coefficient of determinaton (R 2 ), least Mean Square Error (MSE) and highest (Residual Prediction Deviation) RPD: LogBB = 0.0351 + 0.09750868*(logP -2.7414) + 0.00041544 * (MR-83.3191) -0.19660982* (TPSQRT -6.9991) N = 426: R 2 = 0.5711, MSE = 0.1992, RPD = 1.5270 When this PLS model was evaluated on the data that excluded outliers, the correlation coefficient was 0.77 for N=426, with 90.6% of predicted LogBB values within an error of 0.75 and 0.0% with error greater than 1.0. When this model was tested with data that included the outliers, the correlation coefficient was 0.66 for N=474, with 82.3% of predicted LogBB values within an error of 0.75 and 8.2% with error greater than 1.0.