EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction

Yang Zhang, Zhewei Wei*, Ye Yuan, Chongxuan Li, Wenbing Huang*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

Predicting the binding sites of target proteins plays a fundamental role in drug discovery.Most existing deep-learning methods consider a protein as a 3D image by spatially clustering its atoms into voxels and then feed the voxelized protein into a 3D CNN for prediction.However, the CNN-based methods encounter several critical issues: 1) defective in representing irregular protein structures; 2) sensitive to rotations; 3) insufficient to characterize the protein surface; 4) unaware of protein size shift.To address the above issues, this work proposes EquiPocket, an E(3)-equivariant Graph Neural Network (GNN) for binding site prediction, which comprises three modules: the first one to extract local geometric information for each surface atom, the second one to model both the chemical and spatial structure of protein and the last one to capture the geometry of the surface via equivariant message passing over the surface atoms.We further propose a dense attention output layer to alleviate the effect incurred by variable protein size.Extensive experiments on several representative benchmarks demonstrate the superiority of our framework to the state-of-the-art methods.Related codes can be found at the link https://github.com/fengyuewuya/EquiPocket.

Original languageEnglish
Pages (from-to)60021-60039
Number of pages19
JournalProceedings of Machine Learning Research
Volume235
Publication statusPublished - 2024
Event41st International Conference on Machine Learning, ICML 2024 - Vienna, Austria
Duration: 21 Jul 202427 Jul 2024

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