EquiScore: A generic protein-ligand interaction scoring method integrating physical prior knowledge with data augmentation modeling
Duanhua Cao, Geng Chen, Jiaxin Jiang, Jie Yu, Runze Zhang, Mingan Chen, Wei Zhang, Lifan Chen, Feisheng Zhong, Yingying Zhang, Chenghao Lu, Xutong Li, Xiaomin Luo, Sulin Zhang, Mingyue Zheng · bioRxiv (Cold Spring Harbor Laboratory) · 2023
ABSTRACT Developing robust methods for evaluating protein-ligand interactions has been a long-standing problem. Here, we propose a novel approach called EquiScore, which utilizes an equivariant heterogeneous graph neural network to integrate physical prior knowledge and characterize protein-ligand interactions in equivariant geometric space. To improve generalization performance, we constructed a dataset called PDBscreen and designed multiple data augmentation strategies suitable for training scoring methods. We also analyzed potential risks of data leakage in commonly used data-driven modeling processes and proposed a more stringent redundancy removal scheme to alleviate this problem. On two large external test sets, EquiScore outperformed 21 methods across a range of screening performance metrics, and this performance was insensitive to binding pose generation methods. EquiScore also showed good performance on the activity ranking task of a series of structural analogs, indicating its potential to guide lead compound optimization. Finally, we investigated different levels of interpretability of EquiScore, which may provide more insights into structure-based drug design.