NMRBank / README.md
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license: mit

1. NMRBank data (225809)

We batch processed 380,220 NMR segments using NMRExtractor. After removing entries with empty 13C NMR chemical shifts, we obtained about 260,000 entries. Further filtering out entries with empty IUPAC names and NMR chemical shifts resulted in 225,809 entries.

  • NMRBank_data_225809.zip

2. NMRBank data with SMILES (156621)

To normalize these data, we converted IUPAC names to SMILES using ChemDraw and OPSIN, successfully converting 156,621 entries. We then normalized SMILES using RDKit.

  • NMRBank_data_with_SMILES_156621_in_225809.zip

We have normalized the dataset based on standardized SMILES strings. The total number of unique SMILES strings is 149,135.

  • NMRBank_data_with_unique_SMILES_149135_in_156621.zip

For the highest confidence interval (0.6–1), the number of unique SMILES records is 123,174.

  • NMRBank_data_with_unique_SMILES_and_confidence_greater_than_0.6_123174_in_149135.zip

For the highest confidence interval (0.8–1), the number of unique SMILES records is 91,707.

  • NMRBank_data_with_unique_SMILES_and_confidence_greater_than_0.8_91707_in_149135.zip

3. NMRBank dataset in json format

We also provide the NMRBank dataset in JSON format for convenient access and easy viewing.

  • NMRBank_json_format\NMRBank_data_225809_json_format.zip
  • NMRBank_json_format\NMRBank_json_format\NMRBank_data_with_SMILES_156621_in_225809_json_format.zip
  • NMRBank_json_format\NMRBank_data_with_unique_SMILES_149135_in_156621_json_format.zip
  • NMRBank_json_format\NMRBank_data_with_unique_SMILES_and_confidence_greater_than_0.6_123174_in_149135_json_format.zip
  • NMRBank_json_format\NMRBank_data_with_unique_SMILES_and_confidence_greater_than_0.8_91707_in_149135_json_format.zip

4. NMRBank dataset reading and viewing

Read csv file

import pandas as pd
# Reading csv Files
df = pd.read_csv(r"NMRBank_data_225809.csv", encoding="utf-8")
df

Read json file

import pandas as pd
# Reading json Files
df = pd.read_json(r"NMRBank_data_225809.json", orient="records", lines=True)
df