A neural network based prediction of octanol–water partition coefficients using atomic5 fragmental descriptors

An artificial neural network based approach using Atomic5 fragmental descriptors has been developed to predict the octanol–water partition coefficient (logP). We used a pre-selected set of organic molecules from PHYSPROP database as training and test sets for a feedforward neural network. Results demonstrate the superiority of our non-linear model over the traditional linear method.