Update README.md

README.md

@@ -24,10 +24,6 @@ ProFSADB is a large-scale protein-ligand interaction pretraining dataset generat
 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.  
@@ -54,4 +50,9 @@ If you use ProFSADB or the ProFSA method, please cite:
 **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/)
+- **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`.