EquiCPI: SE(3)-Equivariant Geometric Deep Learning for Structure-Aware Prediction of Compound-Protein Interactions

Accurate prediction of compound-protein interactions
(CPI) remains
a cornerstone challenge in computational drug discovery. While existing
sequence-based approaches leverage molecular fingerprints or graph
representations, they critically overlook the three-dimensional (3D)
structural determinants of binding affinity. To bridge this gap, we
present EquiCPI, an end-to-end geometric deep learning framework that
synergizes first-principles structural modeling with SE(3)-equivariant
neural networks. Our pipeline transforms raw sequences into 3D atomic
coordinates via ESMFold for proteins and DiffDock-L for ligands, followed
by physics-guided conformer reranking and equivariant feature learning.
At its core, EquiCPI employs SE(3)-equivariant message passing over
atomic point clouds, preserving symmetry under rotations, translations,
and reflections, while hierarchically encoding local interaction patterns
through tensor products of spherical harmonics. The proposed model
is evaluated on BindingDB (affinity prediction) and DUD-E (virtual
screening). EquiCPI achieves performance on par with or exceeding
the state-of-the-art deep learning competitors.