MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking

• URL: http://arxiv.org/abs/2509.26377v1
• Date: Tue, 30 Sep 2025 15:08:41 GMT
• Title: MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking
• Authors: Siyuan Cao, Hongxuan Wu, Jiabao Brad Wang, Yiliang Yuan, Mustafa Misir,
• Abstract summary: This study introduces an enhanced system, MC-GNNAS-Dock, with three key advances.<n>First, a multi-criteria evaluation integrates binding-pose accuracy (RMSD) with validity checks from PoseBusters, offering a more rigorous assessment.<n>Second, architectural refinements by inclusion of residual connections strengthen predictive robustness.<n>Third, rank-aware loss functions are incorporated to sharpen rank learning.
• Score: 5.313085467302315
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 1\AA{} (2\AA{}) with PoseBuster-validity compared to the single best solver (SBS) Uni-Mol Docking V2.

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