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 ∼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

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