用几何图网络预测药物与靶点亲和力，精度超越现有方法
A Geometric Graph-Based Deep Learning Model for Drug-Target Affinity Prediction
- 基于多尺度加权彩色二分图建模原子间相互作用
- 在CASF-2013/2016上达最新水平，多项指标显著提升
- 对多个数据集保持高精度，适合结构药物设计应用
在基于结构的药物设计中，准确预测候选配体与蛋白受体之间的结合亲和力是一项核心挑战。近年来，人工智能特别是深度学习在该任务上表现出优于传统经验与物理方法的性能，得益于结构和实验亲和力数据的日益丰富。本文提出DeepGGL，一种融合残差连接与注意力机制的深度卷积神经网络，构建于几何图学习框架内。通过利用多尺度加权彩色二分子图，DeepGGL有效捕捉蛋白质-配体复合物中跨多尺度的精细原子级相互作用。我们在CASF-2013和CASF-2016上将DeepGGL与现有模型进行对比，结果显示其在多种评估指标上均达到当前最优表现。为进一步验证鲁棒性与泛化能力，我们还在CSAR-NRC-HiQ数据集和PDBbind v2019留出集上进行了测试，结果表明DeepGGL始终保持高预测精度，凸显其在基于结构的药物发现中结合亲和力预测方面的适应性与可靠性。
原文摘要 · Abstract (English)
In structure-based drug design, accurately estimating the binding affinity between a candidate ligand and its protein receptor is a central challenge. Recent advances in artificial intelligence, particularly deep learning, have demonstrated superior performance over traditional empirical and physics-based methods for this task, enabled by the growing availability of structural and experimental affinity data. In this work, we introduce DeepGGL, a deep convolutional neural network that integrates residual connections and an attention mechanism within a geometric graph learning framework. By leveraging multiscale weighted colored bipartite subgraphs, DeepGGL effectively captures fine-grained atom-level interactions in protein-ligand complexes across multiple scales. We benchmarked DeepGGL against established models on CASF-2013 and CASF-2016, where it achieved state-of-the-art performance with significant improvements across diverse evaluation metrics. To further assess robustness and generalization, we tested the model on the CSAR-NRC-HiQ dataset and the PDBbind v2019 holdout set. DeepGGL consistently maintained high predictive accuracy, highlighting its adaptability and reliability for binding affinity prediction in structure-based drug discovery.
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