Caco-2 and PAMPA permeability assays are both common experimental techniques to measure the permeability of drug-like compounds. It would be useful in medicinal chemistry projects to be able to predict the permeability rates of compounds ahead of time, so as to guide the synthetic chemist toward making orally bioavailable and cell-permeable compounds. Many studies have been done on understanding the permeability of small molecules, but less work has been done on understanding the permeability of larger, more flexible macrocycles. Here we present two computational models for the prediction of 236 non-peptide macrocycles with a low root-mean-square error (RMSE) from corresponding experimental values. Additionally, using the same indicator variables, we present a model that can reliably predict permeability for peptidic macrocycles (N = 75). It is hoped that our models can be used as a synthetic guide to assist drug discovery projects.