Duplicate from jiangteam/DecoyDB

Co-authored-by: Yupu Zhang <YupuZ@users.noreply.huggingface.co>

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README.md

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+---
+task_categories:
+- feature-extraction
+language:
+- en
+tags:
+- biology
+- chemistry
+- drug
+license: apache-2.0
+size_categories:
+- 1M<n<10M
+---
+🔧[Code](https://github.com/spatialdatasciencegroup/DecoyDB), 📂[Dataset](https://huggingface.co/datasets/jiangteam/DecoyDB)
+## 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:
+```plaintext
+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 (y.zhang1@ufl.edu), Department of Computer and Information Science and Engineering, University of Florida 
+- Zhe Jiang (zhe.jiang@ufl.edu), Lead PI, Department of Computer and Information Science and Engineering, University of Florida
+- Chenglong Li (lic@ufl.edu), Department of Medicinal Chemistry, University of Florida
+- Gustavo Seabra (seabra@cop.ufl.edu), Department of Medicinal Chemistry, University of Florida
+- Yanjun Li (yanjun.li@ufl.edu), Department of Medicinal Chemistry, University of Florida
+
+## Citation
+If you use this dataset, please cite our paper:
+
+```bibtex
+@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}
+}

complexes.csv

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+oid sha256:4f35149d793c0367a7833b3f028e2963da7a652a51bd98264861c83ce36c130f
+size 10519870