A molecular-based model for prediction of liquid viscosity of pure organic compounds: A quantitative structure property relationship (QSPR) approach

In this study, a new quantitative structure–property relationship (QSPR) is presented for prediction of the liquid viscosity of pure organic compounds. The model implements eight molecular descriptors selected using the genetic algorithm-based multivariate linear regression (GA-MLR) from more than various 3000 molecular descriptors to predict the liquid viscosity. To propose a comprehensive and predictive model, 2748 pure organic compounds are investigated. Furthermore, several statistical methods are applied to evaluate the predictive capability of the model. The root mean square of error and the average absolute percent error of the model are equal to 0.34 and 7%, respectively.

► A large data set of 2748 data point for liquid viscosity of organic compounds was handled to derive a general QSPR model. ► The linear model implements eight molecular descriptors selected using the GA-MLR strategy. ► The non-linear model employed the eight parameters of the linear model to develop an artificial neural network. ► The root mean square of error and the average absolute percent error of the model are equal to 0.34 and 7%, respectively.