用氢键图与自注意力提升药物靶点亲和力预测精度
HBGSA: Hydrogen Bond Graph with Self-Attention for Drug-Target Binding Affinity Prediction
- 构建氢键空间拓扑图，结合自注意力捕捉远程相互作用
- 在PDBbind和CSAR-HiQ上均超越基线模型，相关系数超0.92
- 适合虚拟筛选中快速识别高亲和力候选药物
精准预测药物-靶点结合亲和力可加速药物发现，优先筛选需实验验证的化合物。现有方法存在三大局限：基于序列的方法忽略空间几何约束，基于结构的方法未利用氢键特征，传统损失函数忽视预测值与目标值的相关性，而后者对虚拟筛选中识别高亲和力化合物至关重要。我们提出HBGSA（氢键图自注意力模型），一个306万参数模型，通过图神经网络编码氢键空间特征，并引入自注意力机制增强远程依赖建模，采用皮尔逊相关系数损失优化。在PDBbind核心集和CSAR-HiQ数据集上的实验表明，HBGSA性能优于基线模型，具备强泛化能力。消融实验验证了氢键建模与皮尔逊损失的有效性。
原文摘要 · Abstract (English)
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.
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