Technology Category
Machine Learning: Graph-based Machine LearningData Mining & Knowledge Management: Graph Mining, Social Network Analysis & CommunityKnowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
HBGSA: Hydrogen Bond Graph with Self-Attention for Drug-Target Binding Affinity Prediction
🤖 AI Summary
This study addresses critical limitations in current drug–target binding affinity prediction methods, which often neglect spatial geometric constraints, hydrogen bonding characteristics, and the correlation between predicted scores and true affinities—hindering the identification of high-affinity compounds. To overcome these issues, this work proposes a novel approach that explicitly models the spatial topology of hydrogen bonds by constructing a hydrogen bond graph, integrates graph neural networks with self-attention mechanisms, and introduces a Pearson correlation loss to enhance prediction consistency. Evaluated on the PDBbind Core Set and CSAR-HiQ benchmarks, the method significantly outperforms existing baselines. Ablation studies confirm the effectiveness of both hydrogen bond modeling and the correlation-aware loss, demonstrating superior generalization and virtual screening performance.
Technology Category
Application Category
📝 Abstract
Accurate prediction of drug-target binding affinity accelerates drug discovery by prioritizing compounds for experimental validation. Current methods face three limitations: sequence-based approaches discard spatial geometric constraints, structure-based methods fail to exploit hydrogen bond features, and conventional loss functions neglect prediction-target correlation, a key factor for identifying high-affinity compounds in virtual screening. We developed HBGSA (Hydrogen Bond Graph with Self-Attention), a 3.06M-parameter model that encodes hydrogen bond spatial features. HBGSA uses graph neural networks to model hydrogen bond spatial topology with self-attention enhancement and Pearson correlation loss. Experimental results on PDBbind Core Set and CSAR-HiQ dataset demonstrate that HBGSA outperforms baseline methods with strong generalization capability. Ablation studies confirm the effectiveness of hydrogen bond modeling and Pearson correlation loss.
Problem
Research questions and friction points this paper is trying to address.
drug-target binding affinity
hydrogen bond
spatial geometry
prediction-target correlation
virtual screening
Innovation
Methods, ideas, or system contributions that make the work stand out.
hydrogen bond graph
self-attention
binding affinity prediction
graph neural network
Pearson correlation loss
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