Two novel topological electro-negativity indices based on distance matrix,named YC and WC indices,were proposed to be used for modeling properties of multiple bond organic compounds by equilibrium electro-negativity o...Two novel topological electro-negativity indices based on distance matrix,named YC and WC indices,were proposed to be used for modeling properties of multiple bond organic compounds by equilibrium electro-negativity of atom and relative bond length of molecule.A quantitative structural property relationship(QSPR) model for estimating flash point of 92 compounds was developed based on the newly introduced topological electro-negativity indices YC and WC and path number parameter P3.The model correlation coefficient and standard error for training set in multiple linear regression were 0.9923 and 5.28,respectively.The average absolute error of flash point was only 3.86 K between experimental and calculated values,and the relative error was 1.46%.Furthermore,the model was strictly tested by both internal and external validations.The predicted values were in good agreement with experimental values for leave-one-out(LOO),and the training set and validation set.The results show that this QSPR model is of good stability and powerful prediction ability.展开更多
文摘Two novel topological electro-negativity indices based on distance matrix,named YC and WC indices,were proposed to be used for modeling properties of multiple bond organic compounds by equilibrium electro-negativity of atom and relative bond length of molecule.A quantitative structural property relationship(QSPR) model for estimating flash point of 92 compounds was developed based on the newly introduced topological electro-negativity indices YC and WC and path number parameter P3.The model correlation coefficient and standard error for training set in multiple linear regression were 0.9923 and 5.28,respectively.The average absolute error of flash point was only 3.86 K between experimental and calculated values,and the relative error was 1.46%.Furthermore,the model was strictly tested by both internal and external validations.The predicted values were in good agreement with experimental values for leave-one-out(LOO),and the training set and validation set.The results show that this QSPR model is of good stability and powerful prediction ability.
