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

Background

Computational protein-ligand modeling is fundamental for drug discovery. However, current benchmarks primarily evaluate models on high-level tasks: whether a protein and ligand bind (binary prediction) and how strongly (affinity regression). This provides limited insight into a model's understanding of the underlying molecular mechanics. The paper argues that a critical missing evaluation is localization—whether a model can pinpoint the specific binding site on a protein and the precise atomic interactions involved in recognition.

Key Points

• The Gap: Existing benchmarks offer limited evidence on whether models can localize binding sites or identify the non-covalent interactions (e.g., hydrogen bonds, hydrophobic contacts) that govern molecular recognition.
• InteractBind's Solution: It is a new large-scale dataset (~100k pairs) and benchmark designed for fine-grained evaluation.
  • Core Task: Binding-Site Localization. This uses protein-residue and ligand-atom interaction maps covering six major types of non-covalent interactions to assess if a model's derived maps correctly localize the binding site.
  • Realistic Generalization Assessment: The dataset includes splits controlled for binding affinity and protein similarity to test model performance under realistic conditions.
• Empirical Findings: The study evaluates eight existing models (sequence-based and interaction-aware) using InteractBind.
  • A key result is the disconnect between high-level and fine-grained performance: models show strong accuracy in predicting whether binding occurs but demonstrate limited capability in localizing the actual binding site.
  • Performance varied significantly across the different non-covalent interaction types, indicating that models do not handle all interaction mechanisms equally well.

Significance

InteractBind fundamentally shifts the evaluation paradigm for protein-ligand modeling. It moves the goalposts from asking "does it bind?" to the more mechanistic and biologically relevant question "where and how does it bind?" By providing a standard benchmark for binding-site localization and interaction analysis, it directly encourages the development of models that are not just predictive but also interpretable and physically grounded. This is crucial for building trustworthy tools in computational drug discovery where understanding the why behind a prediction is as important as the prediction itself.