"""Script for testing selfies against large datasets.
"""
import argparse
import pathlib
import pandas as pd
from rdkit import Chem
from tqdm import tqdm
import selfies as sf
parser = argparse.ArgumentParser()
parser.add_argument("--data_path", type=str, default="version.smi.gz")
parser.add_argument("--col_name", type=str, default="isosmiles")
parser.add_argument("--sep", type=str, default=r"\s+")
parser.add_argument("--start_from", type=int, default=0)
args = parser.parse_args()
TEST_DIR = pathlib.Path(__file__).parent
TEST_SET_PATH = TEST_DIR / "test_sets" / args.data_path
ERROR_LOG_DIR = TEST_DIR / "error_logs"
ERROR_LOG_DIR.mkdir(exist_ok=True, parents=True)
def make_reader():
return pd.read_csv(TEST_SET_PATH, sep=args.sep, chunksize=10000)
def roundtrip_translation():
sf.set_semantic_constraints("hypervalent")
n_entries = 0
for chunk in make_reader():
n_entries += len(chunk)
pbar = tqdm(total=n_entries)
reader = make_reader()
error_log = open(ERROR_LOG_DIR / f"{TEST_SET_PATH.stem}.txt", "a+")
curr_idx = 0
for chunk_idx, chunk in enumerate(reader):
for in_smiles in chunk[args.col_name]:
pbar.update(1)
curr_idx += 1
if curr_idx < args.start_from:
continue
in_smiles = in_smiles.strip()
mol = Chem.MolFromSmiles(in_smiles, sanitize=True)
if (mol is None) or ("*" in in_smiles):
continue
try:
selfies = sf.encoder(in_smiles, strict=True)
out_smiles = sf.decoder(selfies)
except (sf.EncoderError, sf.DecoderError):
error_log.write(in_smiles + "\n")
tqdm.write(in_smiles)
continue
if not is_same_mol(in_smiles, out_smiles):
error_log.write(in_smiles + "\n")
tqdm.write(in_smiles)
error_log.close()
def is_same_mol(smiles1, smiles2):
try:
can_smiles1 = Chem.CanonSmiles(smiles1)
can_smiles2 = Chem.CanonSmiles(smiles2)
return can_smiles1 == can_smiles2
except Exception:
return False
if __name__ == "__main__":
roundtrip_translation()