A Large-Scale Dataset and Benchmark: Do Protein-Ligand Models Learn Binding Sites or Just Binding Likelihood?

2026-05-21

AI Summary

This study addresses the limited fine-grained evaluation of binding sites and non-covalent interactions in existing protein–ligand binding models, which primarily focus on binary binding prediction and affinity estimation. To bridge this gap, the authors introduce InteractBind, a dataset comprising approximately 100,000 protein–ligand complexes, and propose a novel residue-to-atom-level binding site localization task based on six distinct types of non-covalent interactions. They further devise a data splitting strategy that accounts for both binding affinity and protein similarity to rigorously assess model generalization and physical interpretability. Benchmarking eight state-of-the-art models under a unified protocol reveals strong performance in binding prediction but consistently limited accuracy in binding site localization, with substantial performance variation across different interaction types.

Abstract

Protein-ligand modeling underpins computational drug discovery and molecular design. Existing protein-ligand benchmarks typically evaluate whether a protein and ligand interact and how strongly they bind, through tasks such as binary binding prediction and affinity regression. However, these evaluations provide limited evidence of whether models can localize binding sites or identify the non-covalent interactions underlying molecular recognition. To address this gap, we introduce InteractBind, a large-scale protein-ligand dataset comprising approximately 100k protein-ligand pairs, together with a benchmark for fine-grained evaluation. The core fine-grained task is that of binding-site localization, which uses protein-residue and ligand-atom interaction maps spanning six major types of non-covalent interactions to assess whether model-derived interaction maps localize binding sites. InteractBind further includes binding affinity and protein similarity-controlled splits to support realistic generalization assessment. Using InteractBind, we evaluate eight existing sequence-based and interaction-aware models, assessing binary binding prediction and binding-site localization. Results reveal limited binding-site localization despite strong binary binding prediction, with marked variation across non-covalent interaction types. Overall, InteractBind establishes a benchmark paradigm that encourages the development of more interpretable and physically grounded protein-ligand models.

Problem

• protein-ligand binding
• binding site localization
• non-covalent interactions
• molecular recognition
• model interpretability

Innovation

• binding-site localization
• non-covalent interactions
• InteractBind
• protein-ligand modeling
• fine-grained benchmark