A Large-Scale Dataset and Benchmark: Do Protein-Ligand Models Learn Binding Sites or Just Binding Likelihood?
arXiv:2605.24045v1 Announce Type: new 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 appro
The development of more sophisticated AI models demands better benchmarks and datasets to accurately assess their capabilities in complex scientific domains like drug discovery.
Improved protein-ligand modeling directly impacts the speed and efficiency of drug discovery, potentially leading to faster development of new therapeutics and materials.
The introduction of InteractBind shifts the evaluation focus from mere binding prediction to understanding precise binding site localization and molecular interactions, enabling more interpretable and robust AI models.
- · AI-driven drug discovery companies
- · Pharmaceutical research and development
- · Computational biologists
- · AI researchers in scientific discovery
- · Traditional high-throughput screening methods
- · Companies reliant on less precise binding models
More accurate and efficient AI models for structure-based drug design will emerge.
Accelerated discovery of novel drug candidates and materials with specific properties.
Reduced costs and timelines for pharmaceutical development, impacting global health and economic sectors.
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Read at arXiv cs.LG