Graph Learning on Ensembles of Cyclic Peptides: An Investigation of Molecular Ensemble Modeling
Molecular property prediction from structure often uses a single representative conformation, even though many molecules exist as conformational ensembles in solution. We introduce EnsembleEGNN, a molecular ensemble foundation model that encodes an ensemble by first encoding each conformer with shared Equivariant Graph Neural Network (EGNN) layers, then pooling the resulting conformer representations with a Set Attention Block. We pretrain the model on CREMP, a cyclic peptide ensemble dataset, using a multi-task self-supervised objective combining masked token recovery, noisy-coordinate recons
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- PossiblePossibly related (embedding) · 55%Bridging three-dimensional molecular structures and artificial intelligence with a conformation description language →
- PossiblePossibly related (embedding) · 54%Reshaping biomolecular structure prediction through strategic conformational exploration with HelixFold-S1 →
- FuzzySimilar title/name (fuzzy) · 59%tirth8205/code-review-graph →
“Fuzzy title match (0.73): “Graph Learning on Ensembles of Cyclic Peptides: An Investiga” ≈ “tirth8205/code-review-graph””
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “Graph Learning on Ensembles of Cyclic Peptides: An Investiga” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Aaron Feller →
“Graph Learning on Ensembles of Cyclic Peptides: An Investigation of Molecular Ensemble Modeling”
- LinkedLinked via arxiv author · 85%Kris Deibler →
“Graph Learning on Ensembles of Cyclic Peptides: An Investigation of Molecular Ensemble Modeling”
- LinkedLinked via arxiv author · 85%Maxim Secor →
“Graph Learning on Ensembles of Cyclic Peptides: An Investigation of Molecular Ensemble Modeling”