Update README.md

README.md

@@ -64,19 +64,19 @@ The final dataset is suitable for training and evaluating machine learning model
 # Dataset processing
 If you are interested in our dataset curation process, follow these scripts in [our repository](https://huggingface.co/datasets/maomlab/CHAFF/tree/main/CHAFF_processing_scripts).
 
-- st1_download_pubchem.py
+### st1_download_pubchem.py
 Download bioassay datasets from PubChem using a single AID (Assay ID) and save them in CSV format.
 
-- st2_run_download_pubchem.py
+### st2_run_download_pubchem.py
 Automate the download process for multiple AIDs by allowing you to input a list of AIDs, sequentially downloading each dataset.
 
-- st3_extract_active_compounds.py
+### st3_extract_active_compounds.py
 Parse each dataset and filters rows labeled as "Active".
 
-- st4_smiles_curation.py
+### st4_smiles_curation.py
 Standardize and validate 'CanonicalSMILES' column. Apply sanitization, standardization, and fragment removal using RDKit and MolVS.
 
-- st5_detergent_smiles_curation.py
+### st5_detergent_smiles_curation.py
 Special handling for AIDs 585, 584, 1476, 1478, 485341, 485294
 These datasets are processed to remove overlapping compounds based on detergent-related assay pairs. (Without detergent - With detergent).
 This ensures non-specific binders (likely aggregators) are excluded.