Update README.md

README.md

@@ -24,68 +24,6 @@ BiCoA-Net predicts protein-ligand dissociation rate constants (k_off) using bidi
 - Bidirectional co-attention fusion mechanism
 - Trained on curated KineticX datasets
 
-## Quick Start
-```python
-from huggingface_hub import hf_hub_download
-import torch
-
-# Download model weights
-model_path = hf_hub_download(
-    repo_id="Daisyli95/BiCoA-Net",
-    filename="pytorch_model.pt"
-)
-
-# Load model (FP16 format)
-state_dict = torch.load(model_path, map_location='cpu')
-
-# Convert to FP32 for inference (recommended)
-state_dict_fp32 = {k: v.float() if v.dtype == torch.float16 else v 
-                   for k, v in state_dict.items()}
-
-# Load into your model architecture
-model.load_state_dict(state_dict_fp32)
-model.eval()
-```
-
-## Model Details
-
-- **Architecture**: ESM-2 (650M) + MolFormer + Bidirectional Co-Attention
-- **Training Data**: PDBbind v2020 + Custom kinetics data
-- **Format**: PyTorch FP16 (0.96 GB)
-- **Parameters**: ~960M
-- **Input**: 
-  - Protein sequence (FASTA)
-  - Ligand SMILES string
-- **Output**: Predicted log(k_off) value
-
-## Performance
-
-- Concordance Index (C-index): [Add your metrics]
-- Pearson Correlation: [Add your metrics]
-- Test on held-out GPCR targets: [Add your metrics]
-
-## Usage Example
-```python
-# Assuming you have BiCoA-Net model class defined
-from your_model import BiCoANet
-
-# Initialize model
-model = BiCoANet()
-
-# Load pretrained weights
-state_dict = torch.load(model_path, map_location='cpu')
-state_dict = {k: v.float() for k, v in state_dict.items()}
-model.load_state_dict(state_dict)
-model.eval()
-
-# Predict
-protein_seq = "MSLQKEVQKL..."
-ligand_smiles = "CC(C)Cc1ccc(cc1)C(C)C(O)=O"
-
-with torch.no_grad():
-    prediction = model(protein_seq, ligand_smiles)
-    print(f"Predicted log(k_off): {prediction.item()}")
-```
 
 ## Training Details