# Do Deep Learning Models Really Outperform Traditional Approaches in Molecular Docking?

arXiv: https://arxiv.org/abs/2302.07134 · HTML: https://mlsanity.com/paper/2302.07134/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 14 Feb 2023.

## Builds on (most cited first, 5 of 5)

- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking](https://mlsanity.com/paper/2210.01776/) (2022)
- [ChemRL-GEM: Geometry Enhanced Molecular Representation Learning for Property Prediction](https://mlsanity.com/paper/2106.06130/) (2021)
- [EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction](https://mlsanity.com/paper/2202.05146/) (2022)
- [E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking](https://mlsanity.com/paper/2210.06069/) (2022)

## Built on by (most cited first, 11 of 11)

- [Diffusion Models in textitDe Novo Drug Design](https://mlsanity.com/paper/2406.08511/) (2024)
- [DSDP: A Blind Docking Strategy Accelerated by GPUs](https://mlsanity.com/paper/2303.09916/) (2023)
- [PharmacoNet: Accelerating Large-Scale Virtual Screening by Deep Pharmacophore Modeling](https://mlsanity.com/paper/2310.00681/) (2023)
- [Guided Multi-objective Generative AI to Enhance Structure-based Drug Design](https://mlsanity.com/paper/2405.11785/) (2024)
- [Infinite Physical Monkey: Do Deep Learning Methods Really Perform Better in Conformation Generation?](https://mlsanity.com/paper/2304.10494/) (2023)
- [Sifting through the Noise: A Survey of Diffusion Probabilistic Models and Their Applications to Biomolecules](https://mlsanity.com/paper/2406.01622/) (2024)
- [Dockformer: A transformer-based molecular docking paradigm for large-scale virtual screening](https://mlsanity.com/paper/2411.06740/) (2024)
- [Quantum-Inspired Machine Learning for Molecular Docking](https://mlsanity.com/paper/2401.12999/) (2024)
- [GeoDirDock: Guiding Docking Along Geodesic Paths](https://mlsanity.com/paper/2404.06481/) (2024)
- [DeltaDock: A Unified Framework for Accurate, Efficient, and Physically Reliable Molecular Docking](https://mlsanity.com/paper/2410.11224/) (2024)
- [PocketVina Enables Scalable and Highly Accurate Physically Valid Docking through Multi-Pocket Conditioning](https://mlsanity.com/paper/2506.20043/) (2025)

## Abstract (arXiv)

Molecular docking, given a ligand molecule and a ligand binding site (called ``pocket'') on a protein, predicting the binding mode of the protein-ligand complex, is a widely used technique in drug design. Many deep learning models have been developed for molecular docking, while most existing deep learning models perform docking on the whole protein, rather than on a given pocket as the traditional molecular docking approaches, which does not match common needs. What's more, they claim to perform better than traditional molecular docking, but the approach of comparison is not fair, since traditional methods are not designed for docking on the whole protein without a given pocket. In this paper, we design a series of experiments to examine the actual performance of these deep learning models and traditional methods. For a fair comparison, we decompose the docking on the whole protein into two steps, pocket searching and docking on a given pocket, and build pipelines to evaluate traditional methods and deep learning methods respectively. We find that deep learning models are actually good at pocket searching, but traditional methods are better than deep learning models at docking on given pockets. Overall, our work explicitly reveals some potential problems in current deep learning models for molecular docking and provides several suggestions for future works.