Prediction of drug lipophilicity using back propagation artificial neural network modeling

Abstract

A quantitative structure-property relationship (QSPR) study was performed to develop models those relate the structures of 150 drug organic compounds to their n-octanol-water partition coefficients (logPo/w). Molecular descriptors derived solely from 3D structures of the molecular drugs. A genetic algorithm was also applied as a variable selection tools in QSPR analysis. The models were constructed based on 110 training compounds, and predictive ability was tested on 40 compounds reserved for that purpose. Application of the developed models to a testing set of 40 drug organic compounds demonstrates that the new models are reliable with good predictive accuracy and simple formulation. Modeling of logarithm of log Po/w of these compounds as a function of the theoretically derived descriptors was established by artificial neural network (ANN). The neural network employed here is a connected back-propagation model with a 4-4-1 architecture. Four descriptors for these compounds molecular volume (MV) (Geometrical), hydrophilic-lipophilic balance (HLB) (Constitutional), hydrogen bond forming ability (HB) (Electronic) and polar surface area (PSA) (Electrostatic) are taken as inputs for the models. The use of descriptors calculated only from molecular structure eliminates the need for experimental determination of properties for use in the correlation and allows for the estimation of log Po/w for molecules not yet synthesized. The prediction results are in good agreement with the experimental value. The root mean square error of prediction (RMSEP) and square correlation coefficient (R2) for ANN model were 0.1838 0.9876 for the prediction set log Po/w respectively.