Domain-Aware Geometric Multimodal Learning for Multi-Domain Protein-Ligand Affinity Prediction

TL;DR

This paper introduces DAGML, a hierarchical, domain-aware geometric learning framework that improves multi-domain protein-ligand affinity prediction by explicitly modeling domain interfaces and integrating multimodal features.

Contribution

DAGML is a novel hierarchical framework that explicitly models domain modularity and interface signals, enhancing affinity prediction accuracy for multi-domain proteins.

Findings

DAGML reduces mean squared error by 21% over baselines.

Achieves a Pearson correlation of 0.726 in affinity prediction.

Explicit domain interface modeling significantly improves performance.

Abstract

The accurate prediction of protein-ligand binding affinity is important for drug discovery yet remains challenging for multi-domain proteins, where inter-domain dynamics and flexible linkers govern molecular recognition. Current geometric deep learning methods typically treat proteins as monolithic graphs, failing to capture the distinct geometric and energetic signals at domain interfaces. To address this, we introduce DAGML (Domain-Aware Geometric Multimodal Learning), a hierarchical framework that explicitly models domain modularity. DAGML integrates a pre-trained protein language model with a novel domain-aware geometric encoder to distinguish intra- and inter-domain features, while a motif-centric ligand encoder captures pharmacophoric compatibility. We further curate a specialized multi-domain affinity benchmark, classifying complexes by binding topology (e.g., interface vs linker binders). Extensive experiments demonstrate that DAGML achieves a 21% reduction in MSE and a Pearson correlation of 0.726 compared to strong baselines. Ablation studies reveal that explicit modeling of domain interfaces is the primary driver of this improvement, particularly for ligands binding in the clefts between structural units. The code is available at https://github.com/jiankliu/DAGML.