UniSite: The First Cross-Structure Dataset and Learning Framework for End-to-End Ligand Binding Site Detection

Jigang Fan, QuanLin Wu, Shengjie Luo, Liwei Wang

Ligand Binding Site Detection Ligand Binding Site Prediction Drug Discovery Structure Based Drug Discovery

Abstract

The detection of ligand binding sites for proteins is a fundamental step in Structure-Based Drug Design. Despite notable advances in recent years, existing methods, datasets, and evaluation metrics are confronted with several key challenges:

current datasets and methods are centered on individual protein–ligand complexes and neglect that diverse binding sites may exist across multiple complexes of the same protein, introducing significant statistical bias;
ligand binding site detection is typically modeled as a discontinuous workflow, employing binary segmentation and subsequent clustering algorithms;
traditional evaluation metrics do not adequately reflect the actual performance of different binding site prediction methods.

To address these issues, we first introduce UniSite-DS, the first UniProt (Unique Protein)-centric ligand binding site dataset, which contains 4.81 times more multi-site data and 2.08 times more overall data compared to the previously most widely used datasets. We then propose UniSite, the first end-to-end ligand binding site detection framework supervised by set prediction loss with bijective matching.

In addition, we introduce Average Precision based on Intersection over Union (IoU) as a more accurate evaluation metric for ligand binding site prediction. Extensive experiments on UniSite-DS and several representative benchmark datasets demonstrate that IoU-based Average Precision provides a more accurate reflection of prediction quality, and that UniSite outperforms current state-of-the-art methods in ligand binding site detection.

The dataset and codes will be made publicly available at https://github.com/quanlin-wu/unisite.