Learning Protein-Ligand Binding in Hyperbolic Space

• URL: http://arxiv.org/abs/2508.15480v1
• Date: Thu, 21 Aug 2025 11:56:25 GMT
• Title: Learning Protein-Ligand Binding in Hyperbolic Space
• Abstract summary: HypSeek is a hyperbolic representation learning framework that embeds protein pockets and sequences into Lorentz-model hyperbolic space.<n>By leveraging the exponential geometry and negative curvature of hyperbolic space, HypSeek enables expressive, affinity-sensitive embeddings.<n>Our mode unifies virtual screening and affinity ranking in a single framework, introducing a protein-guided three-tower architecture.
• Score: 21.421085367060343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Protein-ligand binding prediction is central to virtual screening and affinity ranking, two fundamental tasks in drug discovery. While recent retrieval-based methods embed ligands and protein pockets into Euclidean space for similarity-based search, the geometry of Euclidean embeddings often fails to capture the hierarchical structure and fine-grained affinity variations intrinsic to molecular interactions. In this work, we propose HypSeek, a hyperbolic representation learning framework that embeds ligands, protein pockets, and sequences into Lorentz-model hyperbolic space. By leveraging the exponential geometry and negative curvature of hyperbolic space, HypSeek enables expressive, affinity-sensitive embeddings that can effectively model both global activity and subtle functional differences-particularly in challenging cases such as activity cliffs, where structurally similar ligands exhibit large affinity gaps. Our mode unifies virtual screening and affinity ranking in a single framework, introducing a protein-guided three-tower architecture to enhance representational structure. HypSeek improves early enrichment in virtual screening on DUD-E from 42.63 to 51.44 (+20.7%) and affinity ranking correlation on JACS from 0.5774 to 0.7239 (+25.4%), demonstrating the benefits of hyperbolic geometry across both tasks and highlighting its potential as a powerful inductive bias for protein-ligand modeling.

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