Bio Molecular Io

Version Compatibility

Reference examples tested with: RDKit 2024.09+, Open Babel 3.1.1+, ChEMBL structure_pipeline 1.2+.

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• CLI: obabel -V; obabel -L formats

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Molecular I/O

Parse, write, and convert molecular file formats. Most downstream errors trace back to silent I/O issues: incorrect aromaticity perception, lost stereochemistry, mishandled charges, dropped stereo bonds, or non-canonical tautomers. This skill enumerates each format's failure modes and prescribes the correct toolchain for each scenario.

For full standardization (canonicalization, salt stripping, tautomer enumeration) see chemoinformatics/molecular-standardization. For generating 3D conformers from parsed 2D molecules, see chemoinformatics/conformer-generation.

Format Taxonomy

Aromaticity Perception (most common silent error)

Different toolkits perceive aromaticity differently. The same SMILES round-tripped between toolkits may produce different canonical strings and different fingerprints.

Fix: Always re-canonicalize via the toolkit doing analysis. If aromaticity must be reassigned explicitly in RDKit, use a concrete model, for example Chem.SetAromaticity(mol, Chem.AromaticityModel.AROMATICITY_RDKIT), after the molecule is in an appropriate sanitized or kekulized state.

Stereochemistry Layers

Stereo loss is the second most common silent error. Each format encodes stereo differently:

• SMILES: @/@@ for tetrahedral, / and \ for cis/trans double bonds
• InChI: /b for double-bond stereo, /t for tetrahedral stereo, and /m plus /s for inversion/overall stereo type
• SDF: wedge/hash bond + parity 0/1/2; cis/trans encoded via bond direction
• MOL2: common Tripos records provide 3D coordinates but no portable wedge or explicit stereo field; verify toolkit perception by round trip

Round-trip tests: If Chem.MolToSmiles(Chem.MolFromSmiles(smi), isomericSmiles=True) does not preserve the represented stereochemistry, inspect whether the source contained stereo markers and whether any step called Chem.RemoveStereochemistry() or discarded stereochemical coordinates/bond directions. Sanitization alone does not intentionally remove valid stereochemistry. If MolFromMolFile returns a molecule missing wedge bonds, inspect the source's coordinates, bond directions, and parity encoding.

Reading SMILES with Stereo Preservation

Goal: Parse SMILES while preserving stereo and aromatic-flag consistency.

Approach: Use Chem.MolFromSmiles(smi) with sanitization on, verify with round-trip canonicalization, and set explicit stereochemistry where the toolkit's perception missed it.

from rdkit import Chem
from rdkit.Chem import AllChem

def parse_smiles_safe(smi):
    mol = Chem.MolFromSmiles(smi)
    if mol is None:
        return None, 'parse_failure'
    Chem.AssignStereochemistry(mol, cleanIt=True, force=True)
    canon = Chem.MolToSmiles(mol)
    round_trip = Chem.MolFromSmiles(canon)
    if Chem.MolToSmiles(round_trip) != canon:
        return mol, 'round_trip_unstable'
    return mol, 'ok'

Reading SDF with Property Carryover

Goal: Load a multi-record SDF preserving per-molecule properties (Name, ID, IC50, etc.) used by downstream filtering and ML labeling.

Approach: Iterate via SDMolSupplier(removeHs=False, sanitize=True), filter None (parse failures), and capture properties via mol.GetPropsAsDict().

from rdkit import Chem

supplier = Chem.SDMolSupplier('library.sdf', removeHs=False, sanitize=True)
mols = []
fails = []
for i, mol in enumerate(supplier):
    if mol is None:
        fails.append(i)
        continue
    props = mol.GetPropsAsDict()
    mols.append((mol, props))
print(f'parsed: {len(mols)}; failed: {len(fails)}')

If a large fraction fails, try sanitize=False then Chem.SanitizeMol(mol, catchErrors=True) to identify per-step failures (kekulization, valence, aromaticity).

Open Babel for MOL2 / PDBQT

RDKit's MOL2 parser is incomplete (SYBYL atom-type sets differ). Open Babel is more robust for MOL2 and PDBQT.

from openbabel import pybel

mols = list(pybel.readfile('mol2', 'ligands.mol2'))
for mol in mols:
    smi = mol.write('smi').strip().split()[0]
    inchi = mol.write('inchi').strip()

For docking output PDBQT, use Open Babel rather than RDKit:

import subprocess
subprocess.run(['obabel', 'docked.pdbqt', '-O', 'docked.sdf'], check=True)

InChI for Canonical Identity

InChI is a standardized, canonical structure identifier designed for cross-database and cross-toolkit interoperability (Heller et al. 2015; O'Boyle 2012). Standard InChI normalizes many mobile-hydrogen tautomers and has limitations for some metal stereochemistry; non-standard options such as /FixedH can distinguish additional representations. InChIKey is a fixed-length hash, so use full InChI or standardized structures when a suspected collision must be resolved (InChI Trust technical FAQ).

from rdkit.Chem.inchi import MolToInchi, MolToInchiKey, InchiToInchiKey

mol = Chem.MolFromSmiles('c1ccc2c(c1)cccc2')
inchi = MolToInchi(mol)
key = MolToInchiKey(mol)

inchi_fixedH, aux_info = Chem.MolToInchiAndAuxInfo(mol, options='/FixedH')

Caveat: Two molecules with identical std InChI may be different tautomers. Use /FixedH for tautomer-distinguishing InChI when needed.

Per-Format Failure Modes

SMILES -- ambiguous aromaticity

Trigger: Input from non-RDKit source (ChemAxon, OpenEye, Daylight) round-tripping into RDKit.

Mechanism: RDKit perceives aromaticity on input. Aromatic flags from origin toolkit are overwritten.

Symptom: Fingerprints differ between toolkits for "identical" molecules; database joins by canonical SMILES miss records.

Fix: Always re-canonicalize within the analysis toolkit. For cross-toolkit identity, use InChIKey not canonical SMILES.