Predictive Modeling of Henry’s Law Constant in Chemical Structures Using LSSVM and ANFIS Algorithms

Abstract

Henry's constants for different existing compounds in water have great importance in transfer calculations. Measurement of these constants face different difficulties including high costs of experiment and low accuracy of measurement apparatus. Due to these facts, proposing a low cost and accurate approach becomes highlighted. To this end, adaptive neuro-fuzzy inference system (ANFIS) and least squares support vector machine (LSSVM) have been used as Henry's constant predictor tools. The molecular structure of compounds has been used as inputs of models. After training the models, the visual and mathematical studies of outputs have been done. The coefficients of determination of LSSVM and ANFIS algorithms are 0.999 and 0.990 respectively. According to the comprehensiveness of databank and accurate prediction of algorithms, it can be concluded that LSSVM and ANFIS algorithms are accurate methods for prediction of Henry's constant in wide range of chemical structure of compounds in water.

Machine Learning Approach for the Estimation of Henry’s Law Constant Based on Molecular Descriptors

“…The authors reported the mean squared error (MSE) and RMSE for the ANFIS model as 0.0072 and 0.0097, respectively. However, they did not provide details about the structural effect on HLC prediction [18].…”