用多标准评估选药靶点对接算法，性能提升超5%。
MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking
- 基于图神经网络，融合多种评价指标选最优对接算法。
- 在PDBBind数据集上，1Å误差内命中率提升5.4%。
- 适合需要高精度对接的药物研发人员使用。
分子对接是药物发现中预测配体-靶点相互作用的核心工具。尽管存在多种基于搜索和机器学习的方法，但无单一算法在所有情境下表现最优，性能随场景变化。为解决此问题，已有基于图神经网络的算法选择框架如GNNAS-Dock被提出。本文提出改进系统MC-GNNAS-Dock，包含三项关键进展：首先，采用多标准评估，整合结合构象精度（RMSD）与PoseBusters有效性验证，实现更严格的评估；其次，通过引入残差连接优化模型架构，增强预测鲁棒性；第三，采用排名感知损失函数，强化排序学习能力。在包含约3200个蛋白-配体复合物的精炼数据集（PDBBind）上进行大量实验，MC-GNNAS-Dock表现出一致更优性能，在复合标准（RMSD低于1Å且经PoseBusters验证）下相比最优单体求解器（SBS）Uni-Mol Docking V2提升达5.4%（在2Å标准下提升3.4%）。
原文摘要 · Abstract (English)
Molecular docking is a core tool in drug discovery for predicting ligand-target interactions. Despite the availability of diverse search-based and machine learning approaches, no single docking algorithm consistently dominates, as performance varies by context. To overcome this challenge, algorithm selection frameworks such as GNNAS-Dock, built on graph neural networks, have been proposed. This study introduces an enhanced system, MC-GNNAS-Dock, with three key advances. First, a multi-criteria evaluation integrates binding-pose accuracy (RMSD) with validity checks from PoseBusters, offering a more rigorous assessment. Second, architectural refinements by inclusion of residual connections strengthen predictive robustness. Third, rank-aware loss functions are incorporated to sharpen rank learning. Extensive experiments are performed on a curated dataset containing approximately 3200 protein-ligand complexes from PDBBind. MC-GNNAS-Dock demonstrates consistently superior performance, achieving up to 5.4% (3.4%) gains under composite criteria of RMSD below 1Å (2Å) with PoseBuster-validity compared to the single best solver (SBS) Uni-Mol Docking V2.
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