Graph Learning on Ensembles of Cyclic Peptides: An Investigation of Molecular Ensemble Modeling

TL;DR - EnsembleEGNN models cyclic peptides as conformational ensembles rather than single structures. Self-supervised pretraining and integration with a sequence encoder substantially improve molecular property prediction.

• Shared EGNN layers encode each conformer, followed by set-attention pooling into one ensemble representation.
• Pretraining combines masked-token recovery, noisy-coordinate reconstruction, and pairwise-distance reconstruction on CREMP.
• Pretraining raises performance from near failure ($R^2=0.005$) to $R^2=0.477$, outperforming sequence-only BERT.
• Joint training with BERT performs best, reaching $R^2=0.538$ and Pearson $r=0.737$.