Graph Learning on Ensembles of Cyclic Peptides: An Investigation of Molecular Ensemble Modeling
Merged summary
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 $r=0.737$.
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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 $r=0.737$.