{"ID":2862853,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.26377","arxiv_id":"2509.26377","title":"MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking","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 1Å (2Å) with PoseBuster-validity compared to the single best solver (SBS) Uni-Mol Docking V2.","short_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...","url_abs":"https://arxiv.org/abs/2509.26377","url_pdf":"https://arxiv.org/pdf/2509.26377v1","authors":"[\"Siyuan Cao\",\"Hongxuan Wu\",\"Jiabao Brad Wang\",\"Yiliang Yuan\",\"Mustafa Misir\"]","published":"2025-09-30T15:08:41Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[\"Graph Neural Network\",\"Generative Adversarial Network\"]","has_code":false}