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---
license: cc-by-4.0
tags:
- ai4science
- aidd
- virtual_screening
- pocket_matching
pretty_name: ProFSADB
---
# ProFSADB
**Dataset Description**
ProFSADB is a large-scale protein-ligand interaction pretraining dataset generated by simulating pocket-ligand complexes from high-resolution protein structures. It addresses the scarcity of experimentally determined protein-ligand complexes (e.g., PDB) by extracting **5+ million non-redundant pocket-pseudo-ligand pairs** through fragmentation and interaction modeling. Each complex mimics ligand-receptor interactions to enable robust pretraining for biomedical tasks like druggability prediction and ligand affinity estimation.
**Dataset Structure**
- **Samples**: Over 5 million complexes.
- **Format**: PDB files containing one complex per file.
- Receptor chain (`R`): Pocket residues surrounding the pseudo-ligand.
- Ligand chain (`L`): Drug-like protein fragment acting as a pseudo-ligand.
- **Stratified Sampling**: Aligned with the PDBBind (v2020) distribution to ensure biological relevance.
**Creation Process**
1. **Fragment Isolation**: Protein structures are segmented into fragments (pseudo-ligands), with terminal corrections to address peptide bond-breaking artifacts.
2. **Pocket Definition**: Excludes the five nearest residues on each fragment side to focus on long-range interactions. Pockets are defined as residues with ≥1 heavy atom within **6Å** of the fragment.
3. **Quality Control**: Complexes are filtered to retain only high-confidence interaction pairs.
**Unique Advantages**
- **Scale**: 50× larger than existing experimental complex datasets (e.g., PDB).
- **Interaction Modeling**: Contrastive pretraining aligns pocket features with pretrained small-molecule representations.
- **Diversity**: Leverages structural variety from protein databases to reduce data bias.
**License**
CC-BY-4.0
**Citation**
If you use ProFSADB or the ProFSA method, please cite:
```
@inproceedings{gao2023self,
title={Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment},
author={Gao, Bowen* and Jia, Yinjun* and Mo, Yuanle and Ni, Yuyan and Ma, Weiying and Ma, Zhiming and Lan, Yanyan†},
booktitle={International Conference on Learning Representations (ICLR)},
year={2023},
url={https://openreview.net/forum?id=uMAujpVi9m}
}
```
**Links**
- **Paper**: [Self-supervised Pocket Pretraining via Protein Fragment-Surroundings Alignment](https://openreview.net/forum?id=uMAujpVi9m)
- **Homepage**: [Project Page](https://atomlab.yanyanlan.com/project/profsa/)
# ProFSA Model Weights
The weights of our best model pretrained using the ProFSADB data is located at `checkpoint_best.pt`. |