Upload v1.2.0 with new CSV files

README.md

@@ -6,9 +6,9 @@ tags:
 - bioassay
 pretty_name: CHAFF
 size_categories:
-- 10K<n<100K
+- 100K<n<1M
 dataset_info:
-- config_name: CHAFF
+- config_name: ChAFF
   features:
   - name: Type
     dtype: string
@@ -19,7 +19,7 @@ dataset_info:
   - name: SMILES
     dtype: string
   splits:
-  - name: train
+  - name: train   
     num_bytes: 5834311
     num_examples: 69777
   download_size: 1913364
@@ -50,58 +50,30 @@ configs:
   - split: train
     path: ChAFF/train-*
 ---
-# Dataset description
-This dataset collection contains ~70K curated active compound lists from 73 different [PubChem](https://pubchem.ncbi.nlm.nih.gov) BioAssay (AID) datasets, focusing on known assay interference artifacts. We downloaded raw assay results from PubChem using their AID identifiers and extracted only the compounds labeled as "Active."
-
-Then We applied SMILES standardization using RDKit and MolVS, including molecule sanitization and fragment removal.
-
-Each dataset includes the following columns:
-
-- Type: Interference type (e.g., Autofluorescence, REDOX)
-- AID: PubChem Assay ID
-- CID: PubChem Compound ID
-(Some CID would not match its SMILES as we did SMILES sanitization)
-- SMILES: Curated chemical representation
-
-The final dataset is suitable for training and evaluating machine learning models.
-
-- If you are looking for a large combined dataset with various AIDs, [click here](https://huggingface.co/datasets/maomlab/CHAFF/tree/1.0.0/CHAFF).
-- If you prefer individual datasets for each AID, [click here](https://huggingface.co/datasets/maomlab/CHAFF/tree/1.0.0/CHAFF_individual_AIDs).
-- Current version of our repository: 1.0.0
-
-
-# List of PubChem AIDs included:
-632, 1641, 1730, 1857, 1926, 435026, 504689, 720541, 1159604,
-587, 588, 589, 590, 591, 592, 593, 594, 709, 923, 1480, 1483, 1696, 1775, 1776, 2124, 2757,
-588517, 588620, 624483, 720675, 720678, 720680, 720681, 720682, 720686, 720687,
-584, 585, 1476, 1478, 485294, 485341, 
-411, 1006, 1269, 1379, 1891, 2515, 2530, 366887, 366889, 366891, 488838, 493175,
-588342, 588498, 602357, 602358, 602364, 602474, 602475, 602476, 602477,
-624030, 652016, 720522, 720835, 1224835, 1347047,
-672, 682, 936,
-878, 888, 929, 1234
-
-
-# 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
-Download bioassay datasets from PubChem using a single AID (Assay ID) and save them in CSV format.
+# ChAFF datasets
+This dataset collection contains ~200K curated Active compound lists from ~90 different BioAssay datasets, focusing on known assay interference artifacts. We applied SMILES standardization using RDKit and MolVS, including molecule sanitization and fragment removal. The final dataset is suitable for training and evaluating machine learning models.
 
-### 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.
+## Types
+- Absorbance
+- Artifact
+- Autofluoresence
+- ColloidalAggregators
+- HeavyHitters
+- LuciferaseInhibition
+- Misannotation
+- Reactivity
+- REDOX
 
-### st3_extract_active_compounds.py
-Parse each dataset and filters rows labeled as "Active".
+## Dataset Columns
 
-### st4_smiles_curation.py
-Standardize and validate 'CanonicalSMILES' column. Apply sanitization, standardization, and fragment removal using RDKit and MolVS.
+| Column     | Description                           |
+|------------|-------------------------------------- |
+| Type       | Task domain (e.g. Absorbance)         |
+| DatasetName| Source dataset name                   |
+| AID        | Pubchem Assay ID                      |
+| ID         | Identifier for the compound           |
+| IDType     | Type of identifier (e.g. CID)         |
+| SMILES     | Curated SMILES                        |
 
-### 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.
 
-- AID 585 → removed 48 compounds also active in AID 584
-- AID 1476 → removed 439 compounds also active in AID 1478
-- AID 485341 → removed 44 compounds also active in AID 485294

prepare_hf_upload.py

@@ -0,0 +1,35 @@
+import os
+import shutil
+
+# Path
+csv_source_folder = "/home/HuggingFaceFinal"
+hf_upload_folder = "/home/HuggingFaceUpload"
+data_folder = os.path.join(hf_upload_folder, "data")
+
+os.makedirs(data_folder, exist_ok=True)
+
+# Curated CSV files
+csv_files = []
+for file in os.listdir(csv_source_folder):
+    if file.endswith(".csv"):
+        src = os.path.join(csv_source_folder, file)
+        dst = os.path.join(data_folder, file)
+        shutil.copy(src, dst)
+        csv_files.append(file)
+
+print(f"Number of copied CSV files: {len(csv_files)}")
+
+
+yaml_path = os.path.join(hf_upload_folder, "dataset.yaml")
+with open(yaml_path, "w") as f:
+    f.write("dataset_info:\n")
+    f.write("  features:\n")
+    for col in ["Type", "DatasetName", "AID", "ID", "IDType", "SMILES"]:
+        f.write(f"    - name: {col}\n")
+        f.write("      dtype: string\n")
+    f.write("  splits:\n")
+    for fname in csv_files:
+        split_name = os.path.splitext(fname)[0]
+        f.write(f"    - name: {split_name}\n")
+
+print("dataset.yaml created")