/datamol

Datamol I/O Module Reference

The datamol.io module provides comprehensive file handling for molecular data across multiple formats.

Reading Molecular Files

dm.read_sdf(filename, sanitize=True, remove_hs=True, as_df=True, mol_column='mol', ...)

Read Structure-Data File (SDF) format.

• Parameters:
  • filename: Path to SDF file (supports local and remote paths via fsspec)
  • sanitize: Apply sanitization to molecules
  • remove_hs: Remove explicit hydrogens
  • as_df: Return as DataFrame (True) or list of molecules (False)
  • mol_column: Name of molecule column in DataFrame
  • n_jobs: Enable parallel processing
• Returns: DataFrame or list of molecules
• Example: df = dm.read_sdf("compounds.sdf")

dm.read_smi(filename, smiles_column='smiles', mol_column='mol', as_df=True, ...)

Read SMILES file (space-delimited by default).

• Common format: SMILES followed by molecule ID/name
• Example: df = dm.read_smi("molecules.smi")

dm.read_csv(filename, smiles_column='smiles', mol_column=None, ...)

Read CSV file with optional automatic SMILES-to-molecule conversion.

• Parameters:
  • smiles_column: Column containing SMILES strings
  • mol_column: If specified, creates molecule objects from SMILES column
• Example: df = dm.read_csv("data.csv", smiles_column="SMILES", mol_column="mol")

dm.read_excel(filename, sheet_name=0, smiles_column='smiles', mol_column=None, ...)

Read Excel files with molecule handling.

• Parameters:
  • sheet_name: Sheet to read (index or name)
  • Other parameters similar to read_csv
• Example: df = dm.read_excel("compounds.xlsx", sheet_name="Sheet1")

dm.read_molblock(molblock, sanitize=True, remove_hs=True)

Parse MOL block string (molecular structure text representation).

dm.read_mol2file(filename, sanitize=True, remove_hs=True, cleanupSubstructures=True)

Read Mol2 format files.

dm.read_pdbfile(filename, sanitize=True, remove_hs=True, proximityBonding=True)

Read Protein Data Bank (PDB) format files.

dm.read_pdbblock(pdbblock, sanitize=True, remove_hs=True, proximityBonding=True)

Parse PDB block string.

dm.open_df(filename, ...)

Universal DataFrame reader - automatically detects format.

• Supported formats: CSV, Excel, Parquet, JSON, SDF
• Example: df = dm.open_df("data.csv") or df = dm.open_df("molecules.sdf")

Writing Molecular Files

dm.to_sdf(mols, filename, mol_column=None, ...)

Write molecules to SDF file.

• Input types:
  • List of molecules
  • DataFrame with molecule column
  • Sequence of molecules
• Parameters:
  • mol_column: Column name if input is DataFrame
• Example:

  dm.to_sdf(mols, "output.sdf")
  # or from DataFrame
  dm.to_sdf(df, "output.sdf", mol_column="mol")

dm.to_smi(mols, filename, mol_column=None, ...)

Write molecules to SMILES file with optional validation.

• Format: SMILES strings with optional molecule names/IDs

dm.to_xlsx(df, filename, mol_columns=None, ...)

Export DataFrame to Excel with rendered molecular images.

• Parameters:
  • mol_columns: Columns containing molecules to render as images
• Special feature: Automatically renders molecules as images in Excel cells
• Example: dm.to_xlsx(df, "molecules.xlsx", mol_columns=["mol"])

dm.to_molblock(mol, ...)

Convert molecule to MOL block string.

dm.to_pdbblock(mol, ...)

Convert molecule to PDB block string.

dm.save_df(df, filename, ...)

Save DataFrame in multiple formats (CSV, Excel, Parquet, JSON).

Remote File Support

All I/O functions support remote file paths through fsspec integration:

• Supported protocols: S3 (AWS), GCS (Google Cloud), Azure, HTTP/HTTPS
• Example:

  dm.read_sdf("s3://bucket/compounds.sdf")
  dm.read_csv("https://example.com/data.csv")

Key Parameters Across Functions

• sanitize: Apply molecule sanitization (default: True)
• remove_hs: Remove explicit hydrogens (default: True)
• as_df: Return DataFrame vs list (default: True for most functions)
• n_jobs: Enable parallel processing (None = all cores, 1 = sequential)
• mol_column: Name of molecule column in DataFrames
• smiles_column: Name of SMILES column in DataFrames