Delta PSA: A New Metric for Conformational Dynamics Underlying Macrocyclic Peptide Permeability

Abstract Development of oral cyclic drugs often suffers from low oral bioavailability resulting from limited passive permeability making accurate prediction a central challenge in drug development. Existing approaches generally fall into two categories: deep learning–based models and accelerated molecular dynamics (aMD) simulations. While deep learning models enable rapid, high-throughput predictions, they often suffer from dependence on training data, and sensitivity to dataset biases. On the other hand, aMD provide mechanistic interpretability by explicitly modeling peptide translocation across lipid bilayers, however, suffer from huge computational cost. Here, we present a conventional MD-based framework for predicting macrocyclic peptide permeability, designed to facilitate interpretation. Importantly, we show that Delta PSA (Δ PSA ) directly quantifies a peptide’s chameleon propensity, providing a mechanistically meaningful measure. Furthermore, we identify two key structural indicators—the sidechain PSA ratio and the radius of gyration—as refined metrics for assessing conformational behavior. When applied to a benchmark dataset of macrocyclic peptides, our framework achieves an MSE of 0.22, surpassing the 0.25 reported for Multi_CycGT and demonstrating its superior performance. Our work also provides a large dataset of MD trajectories for macrocyclic peptides in both polar and nonpolar environments. This dataset offers access to a broader conformational space for cyclic peptide studies. Abstract Figure