QSPR predictions of heat of fusion of organic compounds using bayesian regularized artificial neural networks

Abstract

Computational approaches for the prediction of environmental pollutants' properties have great potential in rapid environmental risk assessment and management with reduced experimental cost. A quantitative structure–property relationship (QSPR) study was conducted to predict the heat of fusion of a set of organic compounds that have adverse effect on the environment. The forward selection (FS) strategy was used for descriptors selection. We examined the feasibility of using multiple linear regression (MLR), artificial neural networks (ANN) and Bayesian regularized artificial neural networks (BRANN) as linear and nonlinear methods. The QSPR models were validated by an external set of compounds that were not used in the model development stage. All models reliably predicted the heat of fusion of the organic compounds under study, whereas more accurate results were obtained by the BRANN model.