Traditional methods for deriving computationally-generated collision cross sections for comparisons with ion mobility-mass spectrometry data require 3-dimensional energy-minimized structures and are often time consuming, preventing high throughput implementation. Here, we introduce a method to predict ion mobility collision cross sections of lipids and peptide analogs important in prebiotic chemistry and other fields. Using less than 100 2-D molecular descriptors this approach resulted in prediction errors of less than 2%.