Generalized Correlative Index, A New Adaptable Molecular Characterization Method and Its Application in Predicting Collision Cross Sections of Singly Protonated Peptides

Abstract

AbstractA novel adaptable and adjustable structure descriptor based on the distribution and interaction of 2D atom pairs is described. The new Generalized Correlative Index (GCI) was applied to a large‐scale dataset of peptide collision cross sections which were previously measured using Ion Mobility Spectrometry (IMS). Based on Genetic Algorithm‐Multiple Linear Regression (GA‐MLR) approach, a linear model relating GCI with peptide collision cross section was constructed and tested using external validation and Monte Carlo cross‐validation, the results confirmed that the GCI‐based model is robust and predictive. The statistics for training and test sets are r=0.9960, q=0.9955, RMSEE=5.217, RMSCV=5.413, rpred=0.9959, and RMSEP=5.016.