bio-molecular-io

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.

1,802 stars

Best use case

bio-molecular-io is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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.

Teams using bio-molecular-io should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/bio-molecular-io/SKILL.md --create-dirs "https://raw.githubusercontent.com/FreedomIntelligence/OpenClaw-Medical-Skills/main/skills/bio-molecular-io/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/bio-molecular-io/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How bio-molecular-io Compares

Feature / Agentbio-molecular-ioStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

## 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

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