KEPLA: A Knowledge-Enhanced Deep Learning Framework for Accurate Protein-Ligand Binding Affinity Prediction

TL;DR

KEPLA is a deep learning framework that integrates biochemical knowledge from Gene Ontology and ligand properties to improve protein-ligand binding affinity prediction, outperforming existing methods and offering interpretability.

Contribution

It introduces a novel approach combining knowledge graph relations with deep learning for more accurate and interpretable binding affinity prediction.

Findings

Outperforms state-of-the-art baselines on benchmark datasets.

Effectively captures domain-specific biochemical insights.

Provides interpretability through knowledge graph relations and attention maps.

Abstract

Accurate prediction of protein-ligand binding affinity is critical for drug discovery. While recent deep learning approaches have demonstrated promising results, they often rely solely on structural features of proteins and ligands, overlooking their valuable biochemical knowledge associated with binding affinity. To address this limitation, we propose KEPLA, a novel deep learning framework that explicitly integrates prior knowledge from Gene Ontology and ligand properties to enhance prediction performance. KEPLA takes protein sequences and ligand molecular graphs as input and optimizes two complementary objectives: (1) aligning global representations with knowledge graph relations to capture domain-specific biochemical insights, and (2) leveraging cross attention between local representations to construct fine-grained joint embeddings for prediction. Experiments on two benchmark datasets across both in-domain and cross-domain scenarios demonstrate that KEPLA consistently outperforms state-of-the-art baselines. Furthermore, interpretability analyses based on knowledge graph relations and cross attention maps provide valuable insights into the underlying predictive mechanisms.