Abstract
Abstract Characterizing the conformational landscapes of G protein-coupled receptors ( GPCR s) is fundamental to understanding signal transduction and accelerating rational drug design. However, current computational approaches often rely on static sequence analysis or lose critical geometric context, failing to resolve the fine-grained structural switches that drive allosteric signaling. Here we introduce Hyaline , a geometric deep learning framework that leverages E(n)-equivariant graph neural networks and ESM3 evolutionary embeddings to predict GPCR activation states directly from 3D coordinates. By incorporating biological priors through motif-specific attention biasing, Hyaline achieves near-perfect classification performance ( AuROC 0.995) on a dataset of 1,590 experimental structures, significantly outperforming sequence-only models in complex cases such as Class C receptors. Hyaline provides a rapid, interpretable framework for annotating receptor conformational states, establishing a scalable foundation for the high-throughput discovery of allosteric modulators in complex signaling landscapes.