MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking
This work addresses the challenge of selecting optimal docking algorithms in drug discovery, offering incremental improvements for researchers in computational chemistry.
The study tackled the problem of inconsistent performance in molecular docking algorithms by developing MC-GNNAS-Dock, which improved algorithm selection with multi-criteria evaluation and architectural refinements, achieving up to 5.4% gains in binding-pose accuracy with validity checks compared to the best single solver.
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.