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

arXiv: https://arxiv.org/abs/2509.26377 · HTML: https://mlsanity.com/paper/2509.26377/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 8 of 8)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking](https://mlsanity.com/paper/2210.01776/) (2022)
- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [Why ResNet Works? Residuals Generalize](https://mlsanity.com/paper/1904.01367/) (2019)
- [Deep Confident Steps to New Pockets: Strategies for Docking Generalization](https://mlsanity.com/paper/2402.18396/) (2024)
- [Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction](https://mlsanity.com/paper/2405.11769/) (2024)
- [PoseX: AI Defeats Physics Approaches on Protein-Ligand Cross Docking](https://mlsanity.com/paper/2505.01700/) (2025)
- [GNNAS-Dock: Budget Aware Algorithm Selection with Graph Neural Networks for Molecular Docking](https://mlsanity.com/paper/2411.12597/) (2024)

## Built on by (most cited first, 3 of 3)

- [Molecular Embedding-Based Algorithm Selection in Protein-Ligand Docking](https://mlsanity.com/paper/2512.02328/) (2025)
- [Graph Neural Network-based Algorithm Selection for the Traveling Salesman Problem: A Systematic Study of Cost and Rank Losses under Distinct Budget Regimes](https://mlsanity.com/paper/2607.18632/) (2026)
- [EEG-AS: Instance-Level Foundation Model Selection for EEG Foundation Models via Behavior Reconstruction](https://mlsanity.com/paper/2609.00653/) (2026)

## Abstract (arXiv)

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 1AA (2AA) with PoseBuster-validity compared to the single best solver (SBS) Uni-Mol Docking V2.