EnsembleCycPerm: Interpretable Modeling of Cyclic Peptide Permeability through Solvent-Dependent Conformational Ensembles

Cyclic peptides are promising therapeutic agents, but their clinical translation is often limited by poor membrane permeability arising from complex, solvent-dependent conformational ensembles. Here, we present a modeling framework inspired by molecular chameleon, integrating sequence and structural information by explicitly representing solvent-dependent ensembles in aqueous and nonpolar environments. The model achieves strong predictive performance (MAE = 0.29, R = 0.85, R 2 = 0.70 on CycPeptMPDB) under random split and retains robust performance under scaffold split evaluation (MAE = 0.34, Pearson R = 0.68). Beyond molecule-level prediction, analysis of one-residue analog-pair in testing set shows that the model captures experimentally meaningful ΔPAMPA trends, supporting its utility for cyclic peptide lead optimization. Model interpretation highlights polarity shielding captured by ΔPSA3D as a key mechanistically interpretable feature associated with permeability. These results demonstrate that permeability emerges from ensemble reorganization rather than a single conformational transition, establishing a generalizable framework for cyclic peptide design and ADMET prediction.