bio-molecular-io
$
npx mdskill add GPTomics/bioSkills/bio-molecular-ioConverts molecular files into standardized RDKit objects
- Loads chemical libraries and prepares molecules for analysis
- Depends on RDKit and Open Babel tools with Python API
- Decides actions by detecting format type via file extension or content signature
- Delivers sanitized, canonicalized molecule objects ready for downstream processing
SKILL.md
.github/skills/bio-molecular-ioView on GitHub ↗
---
name: bio-molecular-io
description: Reads, writes, and converts molecular file formats (SMILES, SDF, MOL2, PDB) using RDKit and Open Babel. Handles structure parsing, canonicalization, and full standardization pipeline including sanitization, normalization, and tautomer canonicalization. Use when loading chemical libraries, converting formats, or preparing molecules for analysis.
tool_type: python
primary_tool: RDKit
---
## Version Compatibility
Reference examples tested with: RDKit 2024.03+
Before using code patterns, verify installed versions match. If versions differ:
- Python: `pip show <package>` then `help(module.function)` to check signatures
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
**"Load my chemical library into Python"** → Parse molecular file formats (SMILES, SDF, MOL2, PDB) into RDKit molecule objects for programmatic access, standardization, and format conversion.
- Python: `Chem.MolFromSmiles()`, `Chem.SDMolSupplier()` (RDKit)
Read, write, and convert molecular file formats with structure standardization.
## Supported Formats
| Format | Extension | Use Case |
|--------|-----------|----------|
| SMILES | .smi | Text representation, databases |
| SDF/MOL | .sdf, .mol | 3D structures, compound libraries |
| MOL2 | .mol2 | Docking, force field atoms |
| PDB | .pdb | Protein-ligand complexes |
## Reading Molecules
**Goal:** Load molecules from SMILES strings, SDF files, or SMILES files into RDKit molecule objects.
**Approach:** Use Chem.MolFromSmiles for individual SMILES, SDMolSupplier for multi-molecule SDF files, and file iteration for SMILES files, filtering out parse failures.
```python
from rdkit import Chem
from rdkit.Chem import AllChem
# From SMILES
mol = Chem.MolFromSmiles('CCO')
# From SDF file (single molecule)
mol = Chem.MolFromMolFile('molecule.mol')
# From SDF file (multiple molecules)
supplier = Chem.SDMolSupplier('library.sdf')
molecules = [mol for mol in supplier if mol is not None]
print(f'Loaded {len(molecules)} molecules')
# From SMILES file
with open('compounds.smi') as f:
molecules = []
for line in f:
parts = line.strip().split()
if parts:
mol = Chem.MolFromSmiles(parts[0])
if mol:
mol.SetProp('_Name', parts[1] if len(parts) > 1 else '')
molecules.append(mol)
```
## Writing Molecules
```python
from rdkit import Chem
# To SMILES
smiles = Chem.MolToSmiles(mol) # Canonical SMILES
smiles_iso = Chem.MolToSmiles(mol, isomericSmiles=True) # With stereochemistry
# To SDF file
writer = Chem.SDWriter('output.sdf')
for mol in molecules:
writer.write(mol)
writer.close()
# To MOL block (string)
mol_block = Chem.MolToMolBlock(mol)
```
## Structure Standardization
**Goal:** Normalize molecular representations to a canonical form for consistent comparison and analysis.
**Approach:** Apply a multi-step pipeline: sanitize valences, normalize functional groups, neutralize charges, canonicalize tautomers, and strip salts using rdMolStandardize.
Use rdMolStandardize module (Python MolStandardize was removed Q1 2024).
```python
from rdkit import Chem
from rdkit.Chem.MolStandardize import rdMolStandardize
def standardize_molecule(mol):
'''
Full standardization pipeline.
Order: Sanitize -> Normalize -> Neutralize -> Canonicalize tautomer -> Strip salts
'''
if mol is None:
return None
# Sanitize (assign valences, kekulize)
try:
Chem.SanitizeMol(mol)
except Exception:
return None
# Normalize (standardize functional groups)
normalizer = rdMolStandardize.Normalizer()
mol = normalizer.normalize(mol)
# Neutralize charges where possible
uncharger = rdMolStandardize.Uncharger()
mol = uncharger.uncharge(mol)
# Canonicalize tautomers
enumerator = rdMolStandardize.TautomerEnumerator()
mol = enumerator.Canonicalize(mol)
# Remove salts/fragments (keep largest)
remover = rdMolStandardize.FragmentRemover()
mol = remover.remove(mol)
return mol
# Standardize a library
standardized = [standardize_molecule(m) for m in molecules]
standardized = [m for m in standardized if m is not None]
```
## Open Babel Conversion
For format conversions not supported by RDKit.
```python
# Open Babel 3.x import (not 'import pybel')
from openbabel import pybel
# Read MOL2 (better supported in Open Babel)
mols = list(pybel.readfile('mol2', 'ligands.mol2'))
# Convert to SDF
output = pybel.Outputfile('sdf', 'output.sdf', overwrite=True)
for mol in mols:
output.write(mol)
output.close()
# Format conversion
for mol in pybel.readfile('pdb', 'complex.pdb'):
mol.write('mol2', 'ligand.mol2', overwrite=True)
```
## Molecular Drawing
Use rdMolDraw2D (legacy Draw.MolToImage deprecated).
```python
from rdkit import Chem
from rdkit.Chem.Draw import rdMolDraw2D
def draw_molecule(mol, filename, size=(400, 300)):
'''Draw molecule to PNG file.'''
drawer = rdMolDraw2D.MolDraw2DCairo(size[0], size[1])
drawer.DrawMolecule(mol)
drawer.FinishDrawing()
with open(filename, 'wb') as f:
f.write(drawer.GetDrawingText())
# Draw with highlighting
def draw_with_substructure(mol, pattern, filename):
'''Highlight substructure match.'''
match = mol.GetSubstructMatch(Chem.MolFromSmarts(pattern))
drawer = rdMolDraw2D.MolDraw2DCairo(400, 300)
drawer.DrawMolecule(mol, highlightAtoms=match)
drawer.FinishDrawing()
with open(filename, 'wb') as f:
f.write(drawer.GetDrawingText())
```
## Related Skills
- molecular-descriptors - Calculate properties after loading
- similarity-searching - Compare loaded molecules
- virtual-screening - Prepare ligands for docking
More from GPTomics/bioSkills
- bio-admet-predictionPredicts ADMET properties using ADMETlab 3.0 API or DeepChem models. Estimates bioavailability, CYP inhibition, hERG liability, and 119 toxicity endpoints with uncertainty quantification. Filters for PAINS and other structural alerts. Use when filtering compounds for drug-likeness or prioritizing leads by predicted safety.
- bio-alignment-amplicon-clippingTrim PCR primers from aligned reads in amplicon-panel BAMs using samtools ampliconclip. Use when processing SARS-CoV-2 ARTIC, hereditary cancer panels, ctDNA hot-spot panels, or any amplicon assay where primer-derived bases would falsely confirm reference at primer footprints.
- bio-alignment-filteringFilter alignments by flags, mapping quality, and regions using samtools view and pysam. Use when extracting specific reads, removing low-quality alignments, or subsetting to target regions.
- bio-alignment-indexingCreate and use BAI/CSI indices for BAM/CRAM files using samtools and pysam. Use when enabling random access to alignment files or fetching specific genomic regions.
- bio-alignment-ioRead, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.
- bio-alignment-msa-parsingParse and analyze multiple sequence alignments using Biopython. Extract sequences, identify conserved regions, analyze gaps, work with annotations, and manipulate alignment data for downstream analysis. Use when parsing or manipulating multiple sequence alignments.
- bio-alignment-msa-statisticsCalculate alignment statistics including sequence identity, conservation scores, substitution matrices, and similarity metrics. Use when comparing alignment quality, measuring sequence divergence, and analyzing evolutionary patterns.
- bio-alignment-multiplePerform multiple sequence alignment using MAFFT, MUSCLE5, ClustalOmega, or T-Coffee. Guides tool and algorithm selection based on dataset size, sequence divergence, and downstream application. Use when aligning three or more homologous sequences for phylogenetics, conservation analysis, or evolutionary studies.
- bio-alignment-pairwisePerform pairwise sequence alignment using Biopython Bio.Align.PairwiseAligner. Use when comparing two sequences, finding optimal alignments, scoring similarity, and identifying local or global matches between DNA, RNA, or protein sequences.
- bio-alignment-sortingSort alignment files by coordinate or read name using samtools and pysam. Use when preparing BAM files for indexing, variant calling, or paired-end analysis.