# The Docking Game: Loop Self-Play for Fast, Dynamic, and Accurate Prediction of Flexible Protein-Ligand Binding

arXiv: https://arxiv.org/abs/2508.05006 · HTML: https://mlsanity.com/paper/2508.05006/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 46 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 15 of 15)

- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [E(n) Equivariant Graph Neural Networks](https://mlsanity.com/paper/2102.09844/) (2021)
- [Spectrally-normalized margin bounds for neural networks](https://mlsanity.com/paper/1706.08498/) (2017)
- [DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking](https://mlsanity.com/paper/2210.01776/) (2022)
- [A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning](https://mlsanity.com/paper/1711.00832/) (2017)
- [EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction](https://mlsanity.com/paper/2202.05146/) (2022)
- [TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery](https://mlsanity.com/paper/2202.08320/) (2022)
- [State-specific protein-ligand complex structure prediction with a multi-scale deep generative model](https://mlsanity.com/paper/2209.15171/) (2022)
- [E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking](https://mlsanity.com/paper/2210.06069/) (2022)
- [Deep Confident Steps to New Pockets: Strategies for Docking Generalization](https://mlsanity.com/paper/2402.18396/) (2024)
- [DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain Packing](https://mlsanity.com/paper/2306.01794/) (2023)
- [FABind: Fast and Accurate Protein-Ligand Binding](https://mlsanity.com/paper/2310.06763/) (2023)
- [Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge](https://mlsanity.com/paper/2402.11459/) (2024)
- [FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation](https://mlsanity.com/paper/2403.20261/) (2024)
- [Fast and Accurate Blind Flexible Docking](https://mlsanity.com/paper/2502.14934/) (2025)

## Built on by (most cited first, 1 of 1)

- [SPACE: Noise Contrastive Estimation Stabilizes Self-Play Fine-Tuning for Large Language Models](https://mlsanity.com/paper/2512.07175/) (2025)

## Abstract (arXiv)

Molecular docking is a crucial aspect of drug discovery, as it predicts the binding interactions between small-molecule ligands and protein pockets. However, current multi-task learning models for docking often show inferior performance in ligand docking compared to protein pocket docking. This disparity arises largely due to the distinct structural complexities of ligands and proteins. To address this issue, we propose a novel game-theoretic framework that models the protein-ligand interaction as a two-player game called the Docking Game, with the ligand docking module acting as the ligand player and the protein pocket docking module as the protein player. To solve this game, we develop a novel Loop Self-Play (LoopPlay) algorithm, which alternately trains these players through a two-level loop. In the outer loop, the players exchange predicted poses, allowing each to incorporate the other's structural predictions, which fosters mutual adaptation over multiple iterations. In the inner loop, each player dynamically refines its predictions by incorporating its own predicted ligand or pocket poses back into its model. We theoretically show the convergence of LoopPlay, ensuring stable optimization. Extensive experiments conducted on public benchmark datasets demonstrate that LoopPlay achieves approximately a 10% improvement in predicting accurate binding modes compared to previous state-of-the-art methods. This highlights its potential to enhance the accuracy of molecular docking in drug discovery.