MULTIPLE LINEAR REGRESSION-ARTIFICIAL NEURAL NETWORK HYBRID MODEL PREDICTING SOLUBILITY INDEX OF ORGANIC COMPOUND

摘要

PURPOSE: A MLR(Multiple Linear Regression)-ANN(Artificial Neuron Network) mixed model for predicting the solubility parameter of an organic compound, is provided to form an ANN outputting the solubility parameter by using a molecule descriptor included in a MLRM(Multiple Linear Regression Model), thereby improving prediction performance. CONSTITUTION: A molecule descriptor value about the solubility parameters of sample organic compounds is prepared. Experimental data is separated into a training set and a test set. An optimum MLRM(Multiple Linear Regression Model) for the training set is explored. The predicted performance of the optimum MLRM is tested on the test set. After an optimum ANNM(Artificial Neural Network Model) divides every samples into three sets, it is explored. If the absolute value of the difference of a solubility parameter prediction value, figured out by the MLRM and the ANNM, is greater than an over-suitability preventing standard value, the solubility parameter prediction value by the MLRM is selected as a solubility parameter value.