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