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

arXiv: https://arxiv.org/abs/2504.04654 · HTML: https://mlsanity.com/paper/2504.04654/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 5 of 5)

- [EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction](https://mlsanity.com/paper/2202.05146/) (2022)
- [The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials](https://mlsanity.com/paper/2205.06643/) (2022)
- [DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences](https://mlsanity.com/paper/1811.02114/) (2018)
- [Deep Confident Steps to New Pockets: Strategies for Docking Generalization](https://mlsanity.com/paper/2402.18396/) (2024)
- [Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians](https://mlsanity.com/paper/2501.09009/) (2025)

## Built on by (most cited first, 2 of 2)

- [DeepChem Equivariant: SE(3)-Equivariant Support in an Open-Source Molecular Machine Learning Library](https://mlsanity.com/paper/2510.16897/) (2025)
- [Spectral Discovery of Continuous Symmetries via Generalized Fourier Transforms](https://mlsanity.com/paper/2603.07299/) (2026)

## Abstract (arXiv)

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 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 re-ranking 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.