Prediction of Melting Point for Drug-like Compounds Using Principal Component-Genetic Algorithm-Artificial Neural Network

摘要

Principal component-genetic algorithm-multiparameter linear regression (PC-GA-MLR) and principal component-genetic algorithm-artificial neural network (PC-GA-ANN) models were applied for prediction of melting point for 323 drag-like compounds.A large number of theoretical descriptors were calculated for each compound.The first 234 principal components (PC's) were found to explain more than 99.9% of variances in the original data matrix.From the pool of these PC's,the genetic algorithm was employed for selection of the best set of extracted PC's for PC-MLR and PC-ANN models.The models were generated using fifteen PC's as variables.For evaluation of the predictive power of the models,melting points of 64 compounds in the prediction set were calculated.Root-mean square errors (RMSE) for PC-GA-MLR and PC-GA-ANN models are 48.18 and 12.77 °C,respectively.Comparison of the results obtained by the models reveals superiority of the PC-GA-ANN relative to the PC-GA-MLR and the recently proposed models (RMSE=40.7 °C).The improvements are due to the fact that the melting point of the compounds demonstrates non-linear correlations with the principal components.