MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909816785469440 |
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| author | Cao, Siyuan Wu, Hongxuan Wang, Jiabao Brad Yuan, Yiliang Misir, Mustafa |
| author_facet | Cao, Siyuan Wu, Hongxuan Wang, Jiabao Brad Yuan, Yiliang Misir, Mustafa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_26377 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking Cao, Siyuan Wu, Hongxuan Wang, Jiabao Brad Yuan, Yiliang Misir, Mustafa Artificial Intelligence 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. |
| title | MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.26377 |