A Large-Scale Dataset and Benchmark: Do Protein-Ligand Models Learn Binding Sites or Just Binding Likelihood?
Protein-ligand modeling underpins computational drug discovery. Existing benchmarks typically evaluate whether a protein and ligand interact and how strongly they bind, but provide limited evidence of whether models can localize binding sites or identify non-covalent interactions. To address this, we introduce InteractBind, a large-scale dataset of ~100k protein-ligand pairs with a benchmark for fine-grained evaluation. The core task is binding-site localization using interaction maps of six non-covalent interaction types. Evaluating eight existing models reveals limited binding-site localization despite strong binary binding prediction, with marked variation across interaction types. InteractBind encourages development of more interpretable and physically grounded models.
Article intelligence
Key points
- InteractBind includes ~100k protein-ligand pairs and a benchmark focused on binding-site localization.
- It uses residue-atom interaction maps covering six non-covalent interaction types to assess model understanding.
- Eight models show strong binary binding prediction but poor localization ability.
- Performance varies across interaction types, highlighting the need for more physically grounded models.
Why it matters
This matters because interactBind includes ~100k protein-ligand pairs and a benchmark focused on binding-site localization.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.24045] A Large-Scale Dataset and Benchmark: Do Protein-Ligand Models Learn Binding Sites or Just Binding Likelihood?
[Submitted on 21 May 2026]
Title:A Large-Scale Dataset and Benchmark: Do Protein-Ligand Models Learn Binding Sites or Just Binding Likelihood?
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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.
Comments: Under Review for the NeurIPS 2026 Conference, Track on Evaluations and Datasets
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.24045 [cs.LG]
(or arXiv:2605.24045v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.24045
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zhaohan Meng [view email] [v1] Thu, 21 May 2026 20:50:03 UTC (3,085 KB)
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