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

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 [22], 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 [2], 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 ∼3200 protein-ligand complexes from PDBBind [15]. MC-GNNAS-Dock demonstrates consistently superior performance, achieving up to 5.4%(3.4%) gains under composite criteria of RMSD below 1 Å(2 Å) with PoseBuster-validity compared to the single best solver (SBS) Uni-Mol Docking V2 [1].

Duke Scholars

Author Mustafa Misir DKU Faculty