Update UsageNotes_Potency.md

UsageNotes_Potency.md

@@ -11,7 +11,7 @@ python3 potency_inference.py
 - ligand_smiles (or SMILES, smiles, canonical_smiles) - Chemical structure in SMILES format
 - protein_sequence (or PROTEIN_SEQ, protein_seq, sequence) - Amino acid sequence
 
-**Optional column:**
+**Optional:**
 - pIC50 (or pic50, PIC50) - Ground truth binding affinity values (enables metric calculation)
 
 ### 2. Neural Network Model Files
@@ -25,18 +25,18 @@ python3 potency_inference.py
 - XGBoost model (.json or .pkl) - Trained gradient boosting model
 - Feature scaler (.pkl) - StandardScaler for descriptor normalization
 - Descriptor list (.txt) - Names of RDKit molecular descriptors
-- Docking scores CSV (optional) - Pre-computed docking scores if model uses them
+- Docking scores CSV (optional) - Pre-computed docking scores
   - Columns: ligand_smiles, protein_sequence, docking_score
 
 ### 4. Stacking Model File
 
 - Ridge regression model (.pth) - Meta-learner that combines predictions
 
-### 5. User Selections (Interactive Prompts)
+### 5. User Selections (Interactive)
 
 - Model type: GNN or GPFT
 - Split strategy: Random or Scaffold (must match training)
-- Whether XGBoost uses docking scores
+- If XGBoost model uses docking scores
 
 ## Generated Outputs