Set Attention Block

EnsembleEGNN: Graph neural network foundation model for cyclic peptide conformational ensemble property prediction

Researchers introduce EnsembleEGNN, a molecular ensemble foundation model that encodes conformational ensembles of cyclic peptides using Equivariant Graph Neural Networks with Set Attention Block pooling. The model is pretrained on the CREMP cyclic peptide ensemble dataset via multi-task self-supervised objectives including masked token recovery and coordinate reconstruction. On the CREMP-CycPeptMPDB benchmark, the pretrained model achieves R²=0.477 versus complete failure (R²=0.005) when trained from scratch, and a hybrid model combining EnsembleEGNN with a BERT sequence encoder reaches R²=0.538, demonstrating that thermodynamically-informed conformational ensemble embeddings meaningfully improve property prediction.