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Use this Skill for chemical file format interconversion (SDF, SMILES, MOL2, PDB), 3D coordinate generation, conformer enumeration, and reaction SMARTS.
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| name | openbabel-conversion |
| description | Use this Skill for chemical file format interconversion (SDF, SMILES, MOL2, PDB), 3D coordinate generation, conformer enumeration, and reaction SMARTS. |
| tags | ["chemistry","cheminformatics","openbabel","rdkit","file-format"] |
| version | 1.0.0 |
| authors | [{"name":"Rosetta Skills Contributors","github":"@xjtulyc"}] |
| license | MIT |
| platforms | ["claude-code","codex","gemini-cli","cursor"] |
| dependencies | {"python":["openbabel>=3.1","rdkit>=2023.3","pandas>=2.0","numpy>=1.24","matplotlib>=3.7"]} |
| last_updated | 2026-03-17 |
| status | stable |
One-line summary: Convert chemical file formats, generate 3D structures, enumerate conformers, and apply reaction SMARTS using OpenBabel and RDKit.
Trigger keywords: format conversion, SDF to SMILES, mol2 PDB, 3D coordinate generation, conformer generation, reaction SMARTS, OpenBabel, obabel
| Format | Use case | Contains 3D? | Common tool |
|---|---|---|---|
| SMILES | Compact line notation | No | Any toolkit |
| SDF/MOL | Structure + properties | Yes | OpenBabel, RDKit |
| MOL2 | Docking preparation | Yes | AMBER, Dock |
| PDB | Protein+ligand | Yes | PyMOL, VMD |
| XYZ | QM input | Yes | Gaussian, ORCA |
| InChI | Canonical identifier | No | IUPAC standard |
Generating 3D coordinates from a 2D SMILES requires:
OpenBabel uses the OBBuilder class for this; RDKit uses the EmbedMolecule/ETKDG algorithm.
For flexible molecules, multiple conformers span the accessible conformational space. The ETKDG (Experimental Torsion angle Knowledge Distance Geometry) method in RDKit produces pharmacophorically relevant conformers.
# OpenBabel (recommended via conda for easiest install)
conda install -c conda-forge openbabel
# RDKit
conda install -c conda-forge rdkit
# Or pip
pip install openbabel-wheel rdkit pandas numpy matplotlib
import openbabel.openbabel as ob
from rdkit import Chem
from rdkit.Chem import AllChem
mol = Chem.MolFromSmiles("CCO")
print(f"Ethanol atoms: {mol.GetNumAtoms()}")
obconv = ob.OBConversion()
print(f"OpenBabel version: {ob.OBReleaseVersion()}")
# Expected: Ethanol atoms: 3
import openbabel.openbabel as ob
def convert_format(input_str, from_fmt, to_fmt, gen3d=False):
"""
Convert a chemical string from one format to another.
Parameters
----------
input_str : str
Input chemical string (SMILES, InChI, etc.)
from_fmt : str
Input format ('smi', 'inchi', 'sdf', 'mol2', 'pdb', 'xyz')
to_fmt : str
Output format
gen3d : bool
If True, generate 3D coordinates before conversion
Returns
-------
str
Converted chemical string
"""
obconv = ob.OBConversion()
obconv.SetInAndOutFormats(from_fmt, to_fmt)
mol = ob.OBMol()
obconv.ReadString(mol, input_str)
if mol.NumAtoms() == 0:
raise ValueError(f"Failed to parse input as {from_fmt}")
if gen3d:
builder = ob.OBBuilder()
builder.Build(mol)
ff = ob.OBForceField.FindForceField("MMFF94")
if ff:
ff.Setup(mol)
ff.ConjugateGradients(500)
ff.GetCoordinates(mol)
mol.AddHydrogens()
return obconv.WriteString(mol)
# Examples
smiles_list = [
"CC(=O)Oc1ccccc1C(=O)O", # Aspirin
"c1ccc(cc1)C(=O)O", # Benzoic acid
"CC12CCC3C(C1CCC2O)CCC4=CC(=O)CCC34C", # Testosterone
]
for smi in smiles_list:
inchi = convert_format(smi, "smi", "inchi")
pdb_3d = convert_format(smi, "smi", "pdb", gen3d=True)
print(f"SMILES: {smi[:30]}...")
print(f" InChI: {inchi.strip()[:50]}...")
print(f" PDB (first line): {pdb_3d.splitlines()[0]}")
print()
from rdkit import Chem
from rdkit.Chem import AllChem, Draw
from rdkit.Chem.rdForceFieldHelpers import MMFFOptimizeMolecule
import numpy as np
def generate_3d_conformers(smiles, n_confs=50, seed=42):
"""
Generate multiple 3D conformers via ETKDG, return lowest-energy one.
Returns
-------
rdkit.Chem.Mol with 3D coordinates, conformer energies array
"""
mol = Chem.MolFromSmiles(smiles)
if mol is None:
raise ValueError(f"Invalid SMILES: {smiles}")
mol = Chem.AddHs(mol)
# ETKDG v3 (best for drug-like molecules)
params = AllChem.ETKDGv3()
params.randomSeed = seed
params.numThreads = 0 # use all cores
params.pruneRmsThresh = 0.5 # remove similar conformers
conf_ids = AllChem.EmbedMultipleConfs(mol, numConfs=n_confs, params=params)
if len(conf_ids) == 0:
raise RuntimeError("ETKDG embedding failed — check SMILES validity")
# MMFF94 optimization
energies = []
for cid in conf_ids:
res = MMFFOptimizeMolecule(mol, confId=cid, maxIters=2000)
if res == 0: # converged
ff = AllChem.MMFFGetMoleculeForceField(mol,
AllChem.MMFFGetMoleculeProperties(mol), confId=cid)
energies.append(ff.CalcEnergy() if ff else float('inf'))
else:
energies.append(float('inf'))
energies = np.array(energies)
best_conf_id = int(conf_ids[np.argmin(energies)])
# Keep only lowest-energy conformer for output
best_mol = Chem.RWMol(mol)
best_mol = best_mol.GetMol()
print(f"Generated {len(conf_ids)} conformers")
print(f"Energy range: {energies.min():.2f} – {energies.max():.2f} kcal/mol")
return best_mol, best_conf_id, energies
# Aspirin
mol_3d, cid, ens = generate_3d_conformers("CC(=O)Oc1ccccc1C(=O)O", n_confs=20)
print(f"Best conformer energy: {ens.min():.2f} kcal/mol")
# Save to SDF
from rdkit.Chem import SDWriter
with SDWriter("aspirin_confs.sdf") as w:
for conf_id in range(mol_3d.GetNumConformers()):
w.write(mol_3d, confId=conf_id)
print("Saved aspirin_confs.sdf")
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem.Draw import MolToImage
import pandas as pd
def apply_reaction(reactant_smiles, rxn_smarts):
"""
Apply a reaction SMARTS to a reactant SMILES.
Returns
-------
list of product SMILES strings
"""
rxn = AllChem.ReactionFromSmarts(rxn_smarts)
if rxn is None:
raise ValueError(f"Invalid reaction SMARTS: {rxn_smarts}")
reactant = Chem.MolFromSmiles(reactant_smiles)
if reactant is None:
raise ValueError(f"Invalid SMILES: {reactant_smiles}")
products = rxn.RunReactants((reactant,))
product_smiles = []
for prod_tuple in products:
for prod in prod_tuple:
try:
Chem.SanitizeMol(prod)
smi = Chem.MolToSmiles(prod)
product_smiles.append(smi)
except Exception:
pass
return list(set(product_smiles)) # deduplicate
# Example reactions
reactions = {
"ester_hydrolysis": "[C:1](=O)[O:2][C:3]>>[C:1](=O)[O:2].[C:3]",
"amide_formation": "[C:1](=O)[OH].[N:2]>>[C:1](=O)[N:2]",
"aromatic_nitration": "c:1:c:c:c:c:c1>>[c:1]c(cc:c:c:c1)[N+](=O)[O-]",
}
test_molecules = {
"ester_hydrolysis": "CC(=O)OCC", # ethyl acetate
"amide_formation": "CC(=O)O", # acetic acid + NH3 (separate reactant)
}
for rxn_name, smarts in reactions.items():
if rxn_name in test_molecules:
reactant = test_molecules[rxn_name]
try:
products = apply_reaction(reactant, smarts)
print(f"\n{rxn_name}:")
print(f" Reactant: {reactant}")
print(f" Products: {products[:3]}")
except Exception as e:
print(f"{rxn_name}: {e}")
# Batch processing
records = []
smiles_batch = [
"CC(=O)Oc1ccccc1C(=O)O", # aspirin
"CC12CCC3C(C1CCC2O)", # steroid skeleton
"c1ccc(cc1)C(=O)O", # benzoic acid
]
for smi in smiles_batch:
mol = Chem.MolFromSmiles(smi)
if mol:
records.append({
"smiles": smi,
"n_atoms": mol.GetNumAtoms(),
"n_rings": mol.GetRingInfo().NumRings(),
"inchi": Chem.MolToInchi(mol),
})
df = pd.DataFrame(records)
print("\nBatch processing results:")
print(df[["smiles", "n_atoms", "n_rings"]].to_string(index=False))
from rdkit import Chem
from rdkit.Chem.Scaffolds import MurckoScaffold
import pandas as pd
def get_murcko_scaffold(smiles, generic=False):
"""
Extract Murcko scaffold (framework) from a molecule.
generic=True returns the carbon skeleton (bemis-murcko generic scaffold).
"""
mol = Chem.MolFromSmiles(smiles)
if mol is None:
return None
scaffold = MurckoScaffold.GetScaffoldForMol(mol)
if generic:
scaffold = MurckoScaffold.MakeScaffoldGeneric(scaffold)
return Chem.MolToSmiles(scaffold)
drugs = {
"Sildenafil": "CCCc1nn(C)c2c(=O)[nH]c(-c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3OCC)nc12",
"Ibuprofen": "CC(C)Cc1ccc(cc1)C(C)C(=O)O",
"Atorvastatin": "CC(C)c1c(C(=O)Nc2ccccc2F)c(-c2ccccc2)c(-c2ccc(F)cc2)n1CCC(O)CC(O)CC(=O)O",
}
scaffold_df = pd.DataFrame([
{"drug": name, "smiles": smi,
"scaffold": get_murcko_scaffold(smi),
"generic_scaffold": get_murcko_scaffold(smi, generic=True)}
for name, smi in drugs.items()
])
print(scaffold_df[["drug", "scaffold"]].to_string(index=False))
Import "openbabel.openbabel" could not be resolvedCause: OpenBabel Python bindings not installed correctly.
Fix:
# Prefer conda installation
conda install -c conda-forge openbabel
# Verify
python -c "import openbabel.openbabel as ob; print(ob.OBReleaseVersion())"
Cause: SMILES has unsupported features (e.g., unusual valence) or stereospecification issues.
Fix:
from rdkit import Chem
mol = Chem.MolFromSmiles(smiles)
# Check for sanitization warnings
from rdkit import RDLogger
RDLogger.DisableLog('rdApp.*') # suppress
mol, problems = Chem.SanitizeMol(mol, catchErrors=True), Chem.DetectChemistryProblems(mol)
print(problems)
| Package | Tested versions | Known issues |
|---|---|---|
| openbabel | 3.1.1, 3.1.4 | Python bindings require manual build on some platforms |
| rdkit | 2023.3, 2024.3 | ETKDG v3 added in 2020 |
# =============================================
# Batch SMILES → SDF with MMFF geometry
# =============================================
import pandas as pd
from rdkit import Chem
from rdkit.Chem import AllChem, SDWriter
from rdkit.Chem.rdForceFieldHelpers import MMFFOptimizeMolecule
smiles_data = pd.DataFrame({
"name": ["Aspirin", "Caffeine", "Dopamine"],
"smiles": ["CC(=O)Oc1ccccc1C(=O)O",
"Cn1cnc2c1c(=O)n(C)c(=O)n2C",
"NCCc1ccc(O)c(O)c1"]
})
with SDWriter("library_3d.sdf") as writer:
for _, row in smiles_data.iterrows():
mol = Chem.MolFromSmiles(row["smiles"])
if mol is None:
print(f"WARN: could not parse {row['name']}")
continue
mol.SetProp("_Name", row["name"])
mol = Chem.AddHs(mol)
AllChem.EmbedMolecule(mol, AllChem.ETKDGv3())
MMFFOptimizeMolecule(mol)
writer.write(mol)
print("Saved library_3d.sdf")
Interpreting these results: The SDF file can be opened in PyMOL, Schrödinger, or AutoDock Vina for molecular docking.
Last updated: 2026-03-17 | Maintainer: @xjtulyc Issues: GitHub Issues