The ability to predict the LogBB of the designed molecules is a pre-requisite for the efficient design of drugs for CNS disorders . Therefore, a variety of methods have been developed for the prediction of LogBB of designed drugs ) . The methods that have been developed for the prediction of LogBB range from fast methods based on calculated physico-chemical descriptors, to more accurate and computationally demanding methods based on molecular dynamics, quantum mechanics, genetic algorithms and neural networks ) . Partial Least Squares (PLS) regression, based on chemical descriptors, such as TPSA, has been demonstrated to be an efficient method for quantitiative prediction of LogBB values . Utilization of non-linear functions of descriptors can improve the correlation between the predicted and observed values of LogBB in regression analysis . This work utilized a linear function that includes the square root of TPSA instead of TPSA as a descriptor for PLS regression for the prediction of LogBB.