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

摘要

PURPOSE: A MLR(Multiple Linear Regression)-ANN(Artificial Neuron Network) mixed model, predicting the water solubility of a pure organic compound, is provided to predict the value of the water solubility even for organic compounds having unknown experimental values, thereby reducing time and cost for an experiment. CONSTITUTION: A molecule descriptor value about the water solubility of a hydrocarbon series organic compound 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 the water solubility prediction value, figured out by the MLRM and the ANNM, is greater than an over-suitability preventing standard value, the water solubility prediction value by the MLRM is selected as a water solubility value.