SolvMorph: Generating Solvent-Dependent Cyclic Peptide Ensembles from Limited Source Conformations

Cyclic peptides can adopt distinct conformational ensembles across solvents, but repeatedly sampling each molecule under every environment is computationally expensive. Inferring a target-solvent ensemble from conformations observed in another solvent requires recovering structures that may be absent from the source ensemble. Fixed-bank reweighting can improve agreement with target observables, but cannot recover missing conformational support. We introduce **SolvMorph**, a source- and target-conditioned Schrödinger bridge that generates target-specific conformations in periodic torsion space, followed by observable-guided probability refinement. On CycPeptMPDB-4D, source conformations improve unseen-solvent prediction, while target-aware generation expands conformational support beyond fixed candidate banks. On CONF192, a generator-naive confirmation panel, SolvMorph improves target-emergent mode recall by 0.26 and 0.35 over source copying for water-to-hexane and hexane-to-water inference, respectively. These gains persist after matching proposal counts and under swapped-target controls, demonstrating recovery of solvent-specific conformational modes from limited source observations. Code is available at https://anonymous.4open.science/r/SolvMorph.