# GNNAS-Dock: Budget Aware Algorithm Selection with Graph Neural Networks for Molecular Docking

arXiv: https://arxiv.org/abs/2411.12597 · HTML: https://mlsanity.com/paper/2411.12597/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking](https://mlsanity.com/paper/2210.01776/) (2022)
- [EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction](https://mlsanity.com/paper/2202.05146/) (2022)
- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [Meta-QSAR: a large-scale application of meta-learning to drug design and discovery](https://mlsanity.com/paper/1709.03854/) (2017)
- [Comparing Algorithm Selection Approaches on Black-Box Optimization Problems](https://mlsanity.com/paper/2306.17585/) (2023)
- [DSDP: A Blind Docking Strategy Accelerated by GPUs](https://mlsanity.com/paper/2303.09916/) (2023)

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

- [MC-GNNAS-Dock: Multi-criteria GNN-based Algorithm Selection for Molecular Docking](https://mlsanity.com/paper/2509.26377/) (2025)
- [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)

## Abstract (arXiv)

Molecular docking is a major element in drug discovery and design. It enables the prediction of ligand-protein interactions by simulating the binding of small molecules to proteins. Despite the availability of numerous docking algorithms, there is no single algorithm consistently outperforms the others across a diverse set of docking scenarios. This paper introduces GNNAS-Dock, a novel Graph Neural Network (GNN)-based automated algorithm selection system for molecular docking in blind docking situations. GNNs are accommodated to process the complex structural data of both ligands and proteins. They benefit from the inherent graph-like properties to predict the performance of various docking algorithms under different conditions. The present study pursues two main objectives: 1) predict the performance of each candidate docking algorithm, in terms of Root Mean Square Deviation (RMSD), thereby identifying the most accurate method for specific scenarios; and 2) choose the best computationally efficient docking algorithm for each docking case, aiming to reduce the time required for docking while maintaining high accuracy. We validate our approach on PDBBind 2020 refined set, which contains about 5,300 pairs of protein-ligand complexes.