PREDICTION OF REDUCED ION MOBILITY CONSTANTS FROM STRUCTURAL INFORMATION USING MULTIPLE LINEAR-REGRESSION ANALYSIS AND COMPUTATIONAL NEURAL NETWORKS

Multiple linear regression analysis and computational neural networks are used to develop models that predict reduced ion mobility constants (K-0) from quantitative structural information encoded as descriptors. The errors associated with the models are similar to the calculated experimental error of similar to 0.040 K-0 units. The best regression model contains five descriptors and has a multiple correlation coefficient (R) value of 0.991 and a standard deviation of 0.0469 K-0 units. The neural network model utilizes the same five descriptors and has a root mean square (RMS) error of 0.0393 K-0 units. The descriptors encode molecular size, weight, functional group, and structural classifications.