jiangteam/DecoyDB

🔧Code, 📂Dataset

Dataset Summary

DecoyDB is a curated dataset of high-resolution protein-ligand complexes and their associated decoy structures. It is designed to support research on graph contrastive learning, binding affinity prediction, and structure-based drug discovery. The dataset is derived from experimentally resolved complexes and refined to ensure data quality.

Predicting the binding affinity of protein-ligand complexes plays a vital role in drug discovery. Unfortunately, progress has been hindered by the lack of large-scale and high-quality binding affinity labels. The widely used PDBbind dataset has fewer than 20K labeled complexes. Self-supervised learning, especially graph contrastive learning (GCL), provides a unique opportunity to break the barrier by pretraining graph neural network models based on vast unlabeled complexes and fine-tuning the models on much fewer labeled complexes. However, the problem faces unique challenges, including a lack of a comprehensive unlabeled dataset with well-defined positive/negative complex pairs and the need to design GCL algorithms that incorporate the unique characteristics of such data. To fill the gap, we propose DecoyDB, a large-scale, structure-aware dataset specifically designed for self-supervised GCL on protein–ligand complexes. DecoyDB consists of high-resolution ground truth complexes and diverse decoy structures with computationally generated binding poses that range from realistic to suboptimal. Each decoy is annotated with a Root Mean Square Deviation (RMSD) from the native pose. We further design a customized GCL framework to pretrain graph neural networks based on DecoyDB and fine-tune the models with labels from PDBbind. Extensive experiments confirm that models pretrained with DecoyDB achieve superior accuracy, sample efficiency, and generalizability.

Data Structure

Each protein-ligand complex is stored in a nested directory under DecoyDB/, using the format:

DecoyDB
├── README.md                                              # This file
├── complexes.csv                                          # Complex ID and paths for the data
├── data.zip                                               # Structures for proteins, ligands and decoys
  ├── {prefix}/                                            # {prefix} = first 2 characters of the complex ID (e.g., '1A', '2B')
  │   └── {complex_id}/                                    # Unique identifier for each complex (e.g., 1A2C_H1Q)
  │       ├── {complex_id}_ligand.pdbqt                    # Ligand structure in AutoDock format
  │       ├── {complex_id}_target.pdbqt                    # Protein structure in AutoDock format
  │       ├── {complex_id}_decoys.pdbqt                    # Concatenated decoy structures
  │       └── {complex_id}_decoys_scores.csv               # Corresponding RMSD scores for each decoy

The data.zip can be found in assets branch.

Dataset Details

Dataset Refinement

To construct DecoyDB, we first filtered protein–ligand complexes from the Protein Data Bank (PDB) with a resolution ≤ 2.5 Å and applied the following refinement steps:

• Removed ligands with molecular weights outside the (50, 1000) range.
• Excluded complexes involving metal clusters, monoatomic ions, and common crystallization molecules.
• Retained ligands with elements limited to C, N, O, H, S, P, and halogens.
• Retained those protein chains with at least one atom within 10 Å of the ligand.
• Saved the ligand and protein separately.

Decoy Generation

For each refined protein–ligand complex, 100 decoy poses were generated using AutoDock Vina 1.2, with a 5 Å padding grid box and an exhaustiveness parameter of 8 and remove unrealistic generated structures.

Dataset Statistics

• Number of protein–ligand complexes: 61,104
• Number of decoys: 5,353,307
• Average number of decoys per complex: 88
• Average RMSD: 7.22 Å
• RMSD range: [0.03, 25.56] Å

Contact

• Yupu Zhang ([email protected]), Department of Computer and Information Science and Engineering, University of Florida
• Zhe Jiang ([email protected]), Lead PI, Department of Computer and Information Science and Engineering, University of Florida
• Chenglong Li ([email protected]), Department of Medicinal Chemistry, University of Florida
• Gustavo Seabra ([email protected]), Department of Medicinal Chemistry, University of Florida
• Yanjun Li ([email protected]), Department of Medicinal Chemistry, University of Florida

Citation

If you use this dataset, please cite our paper:

@inproceedings{
zhang2025decoydb,
title={Decoy{DB}: A Dataset for Graph Contrastive Learning in Protein-Ligand Binding Affinity Prediction},
author={Yupu Zhang and Zelin Xu and Tingsong Xiao and Gustavo Seabra and Yanjun Li and Chenglong Li and Zhe Jiang},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2025},
url={https://openreview.net/forum?id=lzLo5bRgQC}
}