Update README.md

README.md

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----
-license: cc-by-4.0
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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.  
+
+**Intended Use**  
+- Pretrain pocket encoders for interaction-aware protein representation learning.  
+- Downstream tasks: Pocket druggability prediction, ligand binding affinity prediction, pocket matching, and de novo drug design.  
+
+**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/)