# Group Ligands Docking to Protein Pockets

arXiv: https://arxiv.org/abs/2501.15055 · HTML: https://mlsanity.com/paper/2501.15055/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 51 references that are in the map.
- First posted on arXiv 25 Jan 2025.

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

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds](https://mlsanity.com/paper/1802.08219/) (2018)
- [SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks](https://mlsanity.com/paper/2006.10503/) (2020)
- [DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking](https://mlsanity.com/paper/2210.01776/) (2022)
- [Riemannian Score-Based Generative Modelling](https://mlsanity.com/paper/2202.02763/) (2022)
- [e3nn: Euclidean Neural Networks](https://mlsanity.com/paper/2207.09453/) (2022)
- [EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction](https://mlsanity.com/paper/2202.05146/) (2022)
- [Protein-Ligand Scoring with Convolutional Neural Networks](https://mlsanity.com/paper/1612.02751/) (2016)
- [Consistency Regularization for Cross-Lingual Fine-Tuning](https://mlsanity.com/paper/2106.08226/) (2021)
- [E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking](https://mlsanity.com/paper/2210.06069/) (2022)
- [3D Shape Completion with Multi-view Consistent Inference](https://mlsanity.com/paper/1911.12465/) (2019)
- [FABind: Fast and Accurate Protein-Ligand Binding](https://mlsanity.com/paper/2310.06763/) (2023)
- [FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation](https://mlsanity.com/paper/2403.20261/) (2024)
- [Joint alignment of multiple protein-protein interaction networks via convex optimization](https://mlsanity.com/paper/1604.03482/) (2016)

## Built on by (most cited first, 2 of 2)

- [Designing Cyclic Peptides via Harmonic SDE with Atom-Bond Modeling](https://mlsanity.com/paper/2505.21452/) (2025)
- [Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization](https://mlsanity.com/paper/2506.06305/) (2025)

## Abstract (arXiv)

Molecular docking is a key task in computational biology that has attracted increasing interest from the machine learning community. While existing methods have achieved success, they generally treat each protein-ligand pair in isolation. Inspired by the biochemical observation that ligands binding to the same target protein tend to adopt similar poses, we propose textscGroupBind, a novel molecular docking framework that simultaneously considers multiple ligands docking to a protein. This is achieved by introducing an interaction layer for the group of ligands and a triangle attention module for embedding protein-ligand and group-ligand pairs. By integrating our approach with diffusion-based docking model, we set a new S performance on the PDBBind blind docking benchmark, demonstrating the effectiveness of our proposed molecular docking paradigm.