Remove old files

CHAFF_processing_scripts/st1_download_pubchem.py

@@ -1,99 +0,0 @@
-#!/usr/bin/env python3
-"""
-Download canonical SMILES and assay outcome for all compounds in a PubChem BioAssay (AID).
-Uses ListKey interface to handle large assays (>10,000 compounds).
-
-Usage:
-    python download_pubchem.py 584 --output smiles_with_outcome.csv
-"""
-
-import argparse
-import csv
-import sys
-import time
-import requests
-from typing import Dict, Tuple
-from io import StringIO
-
-PUG_BASE = "https://pubchem.ncbi.nlm.nih.gov/rest/pug"
-
-def get_listkey_for_aid(aid: str) -> Tuple[str, int]:
-    """Get ListKey and size for a given AID (used to retrieve large CID sets)."""
-    url = f"{PUG_BASE}/assay/aid/{aid}/cids/JSON?list_return=listkey"
-    try:
-        resp = requests.get(url, timeout=20)
-        resp.raise_for_status()
-        data = resp.json()["IdentifierList"]
-        listkey = data["ListKey"]
-        size = data["Size"]
-        print(f"Retrieved ListKey: {listkey} with {size} CIDs.")
-        return listkey, size
-    except Exception as e:
-        print(f"Error retrieving ListKey for AID {aid}: {e}", file=sys.stderr)
-        sys.exit(1)
-
-def fetch_smiles_from_listkey(listkey: str) -> Dict[int, str]:
-    """Fetch SMILES in batches using ListKey."""
-    smiles_dict = {}
-    url = f"{PUG_BASE}/compound/listkey/{listkey}/property/SMILES/CSV"
-    try:
-        resp = requests.get(url, timeout=30)
-        resp.raise_for_status()
-        reader = csv.DictReader(StringIO(resp.text))
-        count = 0
-        for row in reader:
-            cid = int(row["CID"])
-            smiles = row["SMILES"]
-            smiles_dict[cid] = smiles
-            count += 1
-        print(f"Fetched {count} SMILES")
-    except Exception as e:
-        print(f"Error fetching SMILES: {e}", file=sys.stderr)
-    return smiles_dict
-
-def fetch_outcomes_from_listkey(aid: int, listkey: str, size: int, batch_size: int, delay: float) -> Dict[int, str]:
-    """Fetch outcomes in batches using ListKey."""
-    outcomes = {}
-    for start in range(0, size, batch_size):
-        url = f"{PUG_BASE}/assay/aid/{aid}/CSV?cid=listkey&listkey={listkey}&listkey_start={start}&listkey_count={batch_size}"
-        try:
-            resp = requests.get(url, timeout=30)
-            resp.raise_for_status()
-            reader = csv.DictReader(StringIO(resp.text))
-            for row in reader:
-                if "PUBCHEM_CID" in row and row["PUBCHEM_CID"].isdigit():
-                    cid = int(row["PUBCHEM_CID"])
-                    outcome = row.get("PUBCHEM_ACTIVITY_OUTCOME", "Unavailable")
-                    outcomes[cid] = outcome                
-            print(f"Fetched outcomes for {len(outcomes)} compounds.")
-            time.sleep(delay)
-        except Exception as e:
-            print(f"Error fetching assay outcomes: {e}", file=sys.stderr)
-            sys.exit(1)
-    return outcomes            
-
-def write_to_csv(smiles_dict: Dict[int, str], outcomes: Dict[int, str], output_file: str):
-    """Write combined CID, SMILES, and outcome to CSV."""
-    with open(output_file, "w", newline='') as f:
-        writer = csv.writer(f)
-        writer.writerow(["CID", "CanonicalSMILES", "AssayOutcome"])
-        for cid, smiles in smiles_dict.items():
-            outcome = outcomes.get(cid, "Unavailable")
-            writer.writerow([cid, smiles, outcome])
-    print(f"Saved {len(smiles_dict)} entries to {output_file}")
-
-def main():
-    parser = argparse.ArgumentParser(description="Download SMILES and outcome using ListKey for a PubChem assay")
-    parser.add_argument("aid", help="PubChem Assay ID (AID)")
-    parser.add_argument("--output", "-o", default="smiles_with_outcome.csv", help="Output CSV file")
-    parser.add_argument("--batch-size", type=int, default=10000, help="Batch size for ListKey download")
-    parser.add_argument("--delay", type=float, default=0.5, help="Delay between batches (in seconds)")    
-    args = parser.parse_args()
-
-    listkey, size = get_listkey_for_aid(args.aid)
-    smiles_dict = fetch_smiles_from_listkey(listkey)
-    outcomes = fetch_outcomes_from_listkey(args.aid, listkey, size, batch_size=args.batch_size, delay=args.delay)
-    write_to_csv(smiles_dict, outcomes, args.output)
-
-if __name__ == "__main__":
-    main()

CHAFF_processing_scripts/st2_run_download_pubchem.py

@@ -1,20 +0,0 @@
-import os
-import subprocess
-
-aid_list = [
-    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, # Detergents
-    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
-    ]
-
-for aid in aid_list:
-    output_file = f"./raw/pubchem_aid_{aid}.csv"
-    command = ["python", "st1_download_pubchem.py", str(aid), "--output", output_file]
-    print(f"Running: {' '.join(command)}")
-    subprocess.run(command)

CHAFF_processing_scripts/st3_extract_active_compounds.py

@@ -1,25 +0,0 @@
-import os
-import pandas as pd
-import glob
-
-file_path = "./raw/*.csv"
-
-# Extract active compounds from the datasets
-for file in glob.glob(file_path):
-    name = os.path.splitext(os.path.basename(file))[0]
-    output_path = f"./active/{name}_active.csv"
-
-    if os.path.exists(output_path):
-        print(f"Skipping {name}, already exists.")
-        continue
-
-    raw_df = pd.read_csv(file)
-    active_df = raw_df[raw_df['AssayOutcome'] == 'Active']
-   
-    # Save new CSV file
-    active_df.to_csv(output_path, index=False)
-    print(f"Saved {len(active_df)} active compounds out of {len(raw_df)} to {output_path}")
-
-
-
-

CHAFF_processing_scripts/st4_smiles_curation.py

@@ -1,91 +0,0 @@
-import os
-import pandas as pd
-import rdkit
-import molvs
-import tqdm
-import glob
-from rdkit import Chem
-
-standardizer = molvs.Standardizer()
-fragment_remover = molvs.fragment.FragmentRemover()
-
-
-excluded_aids = {"584", "585", "1478", "1476", "485294", "485341"}  # with/without detergents
-
-file_path = "./active/*.csv"
-
-for file in glob.glob(file_path):
-    file_name = os.path.splitext(os.path.basename(file))[0]
-    aid = file_name.replace("pubchem_aid_", "").replace("_active", "")
-
-    if aid in excluded_aids:
-        print(f"Skipping {aid} (excluded).")
-        continue
-
-    output_path = f"./curated/{file_name}_curated.csv"
-    if os.path.exists(output_path):
-        print(f"Skipping {file_name}, already exists.")
-        continue
-
-    active_df = pd.read_csv(file)
-    smiles_series = active_df["CanonicalSMILES"]
-    active_df["curated_SMILES"] = None
-    cid = active_df["CID"]
-
-    # --------- SMILES sanitization --------- 
-    valid_indices = []
-    invalid_smiles = []
-    warning_smiles = []
-
-    for idx, smiles in smiles_series.items():
-        mol = Chem.MolFromSmiles(smiles)
-        compound_cid = cid.iloc[idx]
-
-        if mol is None:
-            invalid_smiles.append({
-                'CID': compound_cid,
-                'SMILES': smiles,
-                'Reason': "MolFromSmiles returned None"
-            })
-            continue
-
-        results = molvs.validate_smiles(smiles)
-
-        if len(results) > 0:
-            warning_smiles.append({
-                'CID': compound_cid,
-                'SMILES': smiles,
-                'Reason': results
-            })
-            continue
-
-        mol = standardizer.standardize(mol)
-        mol = fragment_remover.remove(mol)
-        standardized = Chem.MolToSmiles(mol)
-
-        active_df.at[idx, "curated_SMILES"] = standardized
-        valid_indices.append(idx)
-
-    # Save valid entries
-    valid_df = active_df.loc[valid_indices].reset_index(drop=True)
-    valid_df = valid_df.drop(columns=["CanonicalSMILES"])
-    valid_df = valid_df.rename(columns={"curated_SMILES": "SMILES"})
-
-    # Create DataFrames for invalid and warning
-    invalid_df = pd.DataFrame(invalid_smiles)
-    warning_df = pd.DataFrame(warning_smiles)
-
-    # Add AID column to dataframes
-    valid_df.insert(0, "AID", aid)
-    invalid_df.insert(0, "AID", aid)
-    warning_df.insert(0, "AID", aid)
-
-    # Save csv files
-    valid_df.to_csv(f'./curated/{file_name}_curated.csv', index=False)
-    invalid_df.to_csv(f'./curated/{file_name}_invalid_smiles.csv', index=False)
-    warning_df.to_csv(f'./curated/{file_name}_molvs_validation.csv', index=False)
-
-    print(f"Number of compounds in {file_name}:", len(active_df))
-    print(f"Number of invalid smiles in {file_name}: {len(invalid_df)}")
-    print(f"Number of warning smiles in {file_name}: {len(warning_df)}\n")
-

CHAFF_processing_scripts/st5_detergent_smiles_curation.py

@@ -1,121 +0,0 @@
-import os
-import pandas as pd
-import rdkit
-import molvs
-import tqdm
-import glob
-from rdkit import Chem
-
-standardizer = molvs.Standardizer()
-fragment_remover = molvs.fragment.FragmentRemover()
-
-
-# AID mapping {without : with detergent}
-filter_map = {
-    "585": "584",
-    "1476": "1478",
-    "485341": "485294"
-}
-
-# AID -> CID set maaping
-cid_sets = {}
-
-# Gather CIDs
-for target_aid in filter_map.values():
-    file_name = f"pubchem_aid_{target_aid}_active.csv"
-    file_full_path = os.path.join("./active", file_name)
-
-    if os.path.exists(file_full_path):
-        df = pd.read_csv(file_full_path)
-        cid_sets[target_aid] = set(df["CID"].tolist())
-    else:
-        print(f"Warning: file for AID {target_aid} not found!")
-
-# Fliter
-for target_aid, filter_aid in filter_map.items():
-    file_name = f"pubchem_aid_{target_aid}_active.csv"
-    file_full_path = os.path.join("./active", file_name)
-
-    if not os.path.exists(file_full_path):
-        print(f"Skipping {target_aid}, file not found.")
-        continue
-
-    df = pd.read_csv(file_full_path)
-
-    before = len(df)
-    df = df[~df["CID"].isin(cid_sets[filter_aid])]
-    after = len(df)
-
-    output_path = f"./active/pubchem_aid_{target_aid}_active_filtered.csv"
-    df.to_csv(output_path, index=False)
-
-    print(f"{target_aid}: removed {before - after} compounds from {before}, saved to {output_path}")
-
-
-filtered_files = [
-    "pubchem_aid_585_active_filtered.csv",
-    "pubchem_aid_1476_active_filtered.csv",
-    "pubchem_aid_485341_active_filtered.csv"
-]
-
-
-for file in filtered_files:
-    file_path = os.path.join("./active", file)
-    file_name = os.path.splitext(file)[0]
-
-    # Read file
-    active_df = pd.read_csv(file_path)
-    smiles_series = active_df["CanonicalSMILES"]
-    active_df["curated_SMILES"] = None
-    cid = active_df["CID"]
-
-    valid_indices = []
-    invalid_smiles = []
-    warning_smiles = []
-
-    for idx, smiles in smiles_series.items():
-        mol = Chem.MolFromSmiles(smiles)
-        compound_cid = cid.iloc[idx]
-
-        if mol is None:
-            invalid_smiles.append({
-                'CID': compound_cid,
-                'SMILES': smiles,
-                'Reason': "MolFromSmiles returned None"
-            })
-            continue
-
-        results = molvs.validate_smiles(smiles)
-        if len(results) > 0:
-            warning_smiles.append({
-                'CID': compound_cid,
-                'SMILES': smiles,
-                'Reason': results
-            })
-            continue
-
-        mol = standardizer.standardize(mol)
-        mol = fragment_remover.remove(mol)
-        standardized = Chem.MolToSmiles(mol)
-
-        active_df.at[idx, "curated_SMILES"] = standardized
-        valid_indices.append(idx)
-
-    # Save outputs
-    valid_df = active_df.loc[valid_indices].reset_index(drop=True)
-    valid_df = valid_df.drop(columns=["CanonicalSMILES"])
-    valid_df = valid_df.rename(columns={"curated_SMILES": "SMILES"})
-    aid = file_name.replace("pubchem_aid_", "").replace("_active_filtered", "")
-    valid_df.insert(0, "AID", aid)
-
-    invalid_df = pd.DataFrame(invalid_smiles)
-    warning_df = pd.DataFrame(warning_smiles)
-    invalid_df.insert(0, "AID", aid)
-    warning_df.insert(0, "AID", aid)
-
-    valid_df.to_csv(f'./curated/{file_name}_curated.csv', index=False)
-    invalid_df.to_csv(f'./curated/{file_name}_invalid_smiles.csv', index=False)
-    warning_df.to_csv(f'./curated/{file_name}_molvs_validation.csv', index=False)
-
-    print(f"Finished curation for {file_name}")
-    print(f"  Valid: {len(valid_df)}, Invalid: {len(invalid_df)}, Warnings: {len(warning_df)}\n")

prepare_hf_upload.py

@@ -1,20 +0,0 @@
-import os
-import shutil
-
-# Path
-csv_source_folder = "./HuggingFaceFinal"
-hf_upload_folder = "./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)}")