rdkit

# RDKit Cheminformatics Toolkit

## Overview

RDKit is a comprehensive cheminformatics library providing Python APIs for molecular analysis and manipulation. This skill provides guidance for reading/writing molecular structures, calculating descriptors, fingerprinting, substructure searching, chemical reactions, 2D/3D coordinate generation, and molecular visualization. Use this skill for drug discovery, computational chemistry, and cheminformatics research tasks.

## Core Capabilities

### 1. Molecular I/O and Creation

**Reading Molecules:**

Read molecular structures from various formats:

```python
from rdkit import Chem

# From SMILES strings
mol = Chem.MolFromSmiles('Cc1ccccc1')  # Returns Mol object or None

# From MOL files
mol = Chem.MolFromMolFile('path/to/file.mol')

# From MOL blocks (string data)
mol = Chem.MolFromMolBlock(mol_block_string)

# From InChI
mol = Chem.MolFromInchi('InChI=1S/C6H6/c1-2-4-6-5-3-1/h1-6H')
```

**Writing Molecules:**

Convert molecules to text representations:

```python
# To canonical SMILES
smiles = Chem.MolToSmiles(mol)

# To MOL block
mol_block = Chem.MolToMolBlock(mol)

# To InChI
inchi = Chem.MolToInchi(mol)
```

**Batch Processing:**

For processing multiple molecules, use Supplier/Writer objects:

```python
# Read SDF files
suppl = Chem.SDMolSupplier('molecules.sdf')
for mol in suppl:
    if mol is not None:  # Check for parsing errors
        # Process molecule
        pass

# Read SMILES files
suppl = Chem.SmilesMolSupplier('molecules.smi', titleLine=False)

# For large files or compressed data
with gzip.open('molecules.sdf.gz') as f:
    suppl = Chem.ForwardSDMolSupplier(f)
    for mol in suppl:
        # Process molecule
        pass

# Multithreaded processing for large datasets
suppl = Chem.MultithreadedSDMolSupplier('molecules.sdf')

# Write molecules to SDF
writer = Chem.SDWriter('output.sdf')
for mol in molecules:
    writer.write(mol)
writer.close()
```

**Important Notes:**
- All `MolFrom*` functions return `None` on failure with error messages
- Always check for `None` before processing molecules
- Molecules are automatically sanitized on import (validates valence, perceives aromaticity)

### 2. Molecular Sanitization and Validation

RDKit automatically sanitizes molecules during parsing, executing 13 steps including valence checking, aromaticity perception, and chirality assignment.

**Sanitization Control:**

```python
# Disable automatic sanitization
mol = Chem.MolFromSmiles('C1=CC=CC=C1', sanitize=False)

# Manual sanitization
Chem.SanitizeMol(mol)

# Detect problems before sanitization
problems = Chem.DetectChemistryProblems(mol)
for problem in problems:
    print(problem.GetType(), problem.Message())

# Partial sanitization (skip specific steps)
from rdkit.Chem import rdMolStandardize
Chem.SanitizeMol(mol, sanitizeOps=Chem.SANITIZE_ALL ^ Chem.SANITIZE_PROPERTIES)
```

**Common Sanitization Issues:**
- Atoms with explicit valence exceeding maximum allowed will raise exceptions
- Invalid aromatic rings will cause kekulization errors
- Radical electrons may not be properly assigned without explicit specification

### 3. Molecular Analysis and Properties

**Accessing Molecular Structure:**

```python
# Iterate atoms and bonds
for atom in mol.GetAtoms():
    print(atom.GetSymbol(), atom.GetIdx(), atom.GetDegree())

for bond in mol.GetBonds():
    print(bond.GetBeginAtomIdx(), bond.GetEndAtomIdx(), bond.GetBondType())

# Ring information
ring_info = mol.GetRingInfo()
ring_info.NumRings()
ring_info.AtomRings()  # Returns tuples of atom indices

# Check if atom is in ring
atom = mol.GetAtomWithIdx(0)
atom.IsInRing()
atom.IsInRingSize(6)  # Check for 6-membered rings

# Find smallest set of smallest rings (SSSR)
from rdkit.Chem import GetSymmSSSR
rings = GetSymmSSSR(mol)
```

**Stereochemistry:**

```python
# Find chiral centers
from rdkit.Chem import FindMolChiralCenters
chiral_centers = FindMolChiralCenters(mol, includeUnassigned=True)
# Returns list of (atom_idx, chirality) tuples

# Assign stereochemistry from 3D coordinates
from rdkit.Chem import AssignStereochemistryFrom3D
AssignStereochemistryFrom3D(mol)

# Check bond stereochemistry
bond = mol.GetBondWithIdx(0)
stereo = bond.GetStereo()  # STEREONONE, STEREOZ, STEREOE, etc.
```

**Fragment Analysis:**

```python
# Get disconnected fragments
frags = Chem.GetMolFrags(mol, asMols=True)

# Fragment on specific bonds
from rdkit.Chem import FragmentOnBonds
frag_mol = FragmentOnBonds(mol, [bond_idx1, bond_idx2])

# Count ring systems
from rdkit.Chem.Scaffolds import MurckoScaffold
scaffold = MurckoScaffold.GetScaffoldForMol(mol)
```

### 4. Molecular Descriptors and Properties

**Basic Descriptors:**

```python
from rdkit.Chem import Descriptors

# Molecular weight
mw = Descriptors.MolWt(mol)
exact_mw = Descriptors.ExactMolWt(mol)

# LogP (lipophilicity)
logp = Descriptors.MolLogP(mol)

# Topological polar surface area
tpsa = Descriptors.TPSA(mol)

# Number of hydrogen bond donors/acceptors
hbd = Descriptors.NumHDonors(mol)
hba = Descriptors.NumHAcceptors(mol)

# Number of rotatable bonds
rot_bonds = Descriptors.NumRotatableBonds(mol)

# Number of aromatic rings
aromatic_rings = Descriptors.NumAromaticRings(mol)
```

**Batch Descriptor Calculation:**

```python
# Calculate all descriptors at once
all_descriptors = Descriptors.CalcMolDescriptors(mol)
# Returns dictionary: {'MolWt': 180.16, 'MolLogP': 1.23, ...}

# Get list of available descriptor names
descriptor_names = [desc[0] for desc in Descriptors._descList]
```

**Lipinski's Rule of Five:**

```python
# Check drug-likeness
mw = Descriptors.MolWt(mol) <= 500
logp = Descriptors.MolLogP(mol) <= 5
hbd = Descriptors.NumHDonors(mol) <= 5
hba = Descriptors.NumHAcceptors(mol) <= 10

is_drug_like = mw and logp and hbd and hba
```

### 5. Fingerprints and Molecular Similarity

**Fingerprint Types:**

```python
from rdkit.Chem import AllChem, RDKFingerprint
from rdkit.Chem.AtomPairs import Pairs, Torsions
from rdkit.Chem import MACCSkeys

# RDKit topological fingerprint
fp = Chem.RDKFingerprint(mol)

# Morgan fingerprints (circular fingerprints, similar to ECFP)
fp = AllChem.GetMorganFingerprint(mol, radius=2)
fp_bits = AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, nBits=2048)

# MACCS keys (166-bit structural key)
fp = MACCSkeys.GenMACCSKeys(mol)

# Atom pair fingerprints
fp = Pairs.GetAtomPairFingerprint(mol)

# Topological torsion fingerprints
fp = Torsions.GetTopologicalTorsionFingerprint(mol)

# Avalon fingerprints (if available)
from rdkit.Avalon import pyAvalonTools
fp = pyAvalonTools.GetAvalonFP(mol)
```

**Similarity Calculation:**

```python
from rdkit import DataStructs

# Calculate Tanimoto similarity
fp1 = AllChem.GetMorganFingerprintAsBitVect(mol1, radius=2)
fp2 = AllChem.GetMorganFingerprintAsBitVect(mol2, radius=2)
similarity = DataStructs.TanimotoSimilarity(fp1, fp2)

# Calculate similarity for multiple molecules
similarities = DataStructs.BulkTanimotoSimilarity(fp1, [fp2, fp3, fp4])

# Other similarity metrics
dice = DataStructs.DiceSimilarity(fp1, fp2)
cosine = DataStructs.CosineSimilarity(fp1, fp2)
```

**Clustering and Diversity:**

```python
# Butina clustering based on fingerprint similarity
from rdkit.ML.Cluster import Butina

# Calculate distance matrix
dists = []
fps = [AllChem.GetMorganFingerprintAsBitVect(mol, 2) for mol in mols]
for i in range(len(fps)):
    sims = DataStructs.BulkTanimotoSimilarity(fps[i], fps[:i])
    dists.extend([1-sim for sim in sims])

# Cluster with distance cutoff
clusters = Butina.ClusterData(dists, len(fps), distThresh=0.3, isDistData=True)
```

### 6. Substructure Searching and SMARTS

**Basic Substructure Matching:**

```python
# Define query using SMARTS
query = Chem.MolFromSmarts('[#6]1:[#6]:[#6]:[#6]:[#6]:[#6]:1')  # Benzene ring

# Check if molecule contains substructure
has_match = mol.HasSubstructMatch(query)

# Get all matches (returns tuple of tuples with atom indices)
matches = mol.GetSubstructMatches(query)

# Get only first match
match = mol.GetSubstructMatch(query)
```

**Common SMARTS Patterns:**

```python
# Primary alcohols
primary_alcohol = Chem.MolFromSmarts('[CH2][OH1]')

# Carboxylic acids
carboxylic_acid = Chem.MolFromSmarts('C(=O)[OH]')

# Amides
amide = Chem.MolFromSmarts('C(=O)N')

# Aromatic heterocycles
aromatic_n = Chem.MolFromSmarts('[nR]')  # Aromatic nitrogen in ring

# Macrocycles (rings > 12 atoms)
macrocycle = Chem.MolFromSmarts('[r{12-}]')
```

**Matching Rules:**
- Unspecified properties in query match any value in target
- Hydrogens are ignored unless explicitly specified
- Charged query atom won't match uncharged target atom
- Aromatic query atom won't match aliphatic target atom (unless query is generic)

### 7. Chemical Reactions

**Reaction SMARTS:**

```python
from rdkit.Chem import AllChem

# Define reaction using SMARTS: reactants >> products
rxn = AllChem.ReactionFromSmarts('[C:1]=[O:2]>>[C:1][O:2]')  # Ketone reduction

# Apply reaction to molecules
reactants = (mol1,)
products = rxn.RunReactants(reactants)

# Products is tuple of tuples (one tuple per product set)
for product_set in products:
    for product in product_set:
        # Sanitize product
        Chem.SanitizeMol(product)
```

**Reaction Features:**
- Atom mapping preserves specific atoms between reactants and products
- Dummy atoms in products are replaced by corresponding reactant atoms
- "Any" bonds inherit bond order from reactants
- Chirality preserved unless explicitly changed

**Reaction Similarity:**

```python
# Generate reaction fingerprints
fp = AllChem.CreateDifferenceFingerprintForReaction(rxn)

# Compare reactions
similarity = DataStructs.TanimotoSimilarity(fp1, fp2)
```

### 8. 2D and 3D Coordinate Generation

**2D Coordinate Generation:**

```python
from rdkit.Chem import AllChem

# Generate 2D coordinates for depiction
AllChem.Compute2DCoords(mol)

# Align molecule to template structure
template = Chem.MolFromSmiles('c1ccccc1')
AllChem.Compute2DCoords(template)
AllChem.GenerateDepictionMatching2DStructure(mol, template)
```

**3D Coordinate Generation and Conformers:**

```python
# Generate single 3D conformer using ETKDG
AllChem.EmbedMolecule(mol, randomSeed=42)

# Generate multiple conformers
conf_ids = AllChem.EmbedMultipleConfs(mol, numConfs=10, randomSeed=42)

# Optimize geometry with force field
AllChem.UFFOptimizeMolecule(mol)  # UFF force field
AllChem.MMFFOptimizeMolecule(mol)  # MMFF94 force field

# Optimize all conformers
for conf_id in conf_ids:
    AllChem.MMFFOptimizeMolecule(mol, confId=conf_id)

# Calculate RMSD between conformers
from rdkit.Chem import AllChem
rms = AllChem.GetConformerRMS(mol, conf_id1, conf_id2)

# Align molecules
AllChem.AlignMol(probe_mol, ref_mol)
```

**Constrained Embedding:**

```python
# Embed with part of molecule constrained to specific coordinates
AllChem.ConstrainedEmbed(mol, core_mol)
```

### 9. Molecular Visualization

**Basic Drawing:**

```python
from rdkit.Chem import Draw

# Draw single molecule to PIL image
img = Draw.MolToImage(mol, size=(300, 300))
img.save('molecule.png')

# Draw to file directly
Draw.MolToFile(mol, 'molecule.png')

# Draw multiple molecules in grid
mols = [mol1, mol2, mol3, mol4]
img = Draw.MolsToGridImage(mols, molsPerRow=2, subImgSize=(200, 200))
```

**Highlighting Substructures:**

```python
# Highlight substructure match
query = Chem.MolFromSmarts('c1ccccc1')
match = mol.GetSubstructMatch(query)

img = Draw.MolToImage(mol, highlightAtoms=match)

# Custom highlight colors
highlight_colors = {atom_idx: (1, 0, 0) for atom_idx in match}  # Red
img = Draw.MolToImage(mol, highlightAtoms=match,
                      highlightAtomColors=highlight_colors)
```

**Customizing Visualization:**

```python
from rdkit.Chem.Draw import rdMolDraw2D

# Create drawer with custom options
drawer = rdMolDraw2D.MolDraw2DCairo(300, 300)
opts = drawer.drawOptions()

# Customize options
opts.addAtomIndices = True
opts.addStereoAnnotation = True
opts.bondLineWidth = 2

# Draw molecule
drawer.DrawMolecule(mol)
drawer.FinishDrawing()

# Save to file
with open('molecule.png', 'wb') as f:
    f.write(drawer.GetDrawingText())
```

**Jupyter Notebook Integration:**

```python
# Enable inline display in Jupyter
from rdkit.Chem.Draw import IPythonConsole

# Customize default display
IPythonConsole.ipython_useSVG = True  # Use SVG instead of PNG
IPythonConsole.molSize = (300, 300)   # Default size

# Molecules now display automatically
mol  # Shows molecule image
```

**Visualizing Fingerprint Bits:**

```python
# Show what molecular features a fingerprint bit represents
from rdkit.Chem import Draw

# For Morgan fingerprints
bit_info = {}
fp = AllChem.GetMorganFingerprintAsBitVect(mol, radius=2, bitInfo=bit_info)

# Draw environment for specific bit
img = Draw.DrawMorganBit(mol, bit_id, bit_info)
```

### 10. Molecular Modification

**Adding/Removing Hydrogens:**

```python
# Add explicit hydrogens
mol_h = Chem.AddHs(mol)

# Remove explicit hydrogens
mol = Chem.RemoveHs(mol_h)
```

**Kekulization and Aromaticity:**

```python
# Convert aromatic bonds to alternating single/double
Chem.Kekulize(mol)

# Set aromaticity
Chem.SetAromaticity(mol)
```

**Replacing Substructures:**

```python
# Replace substructure with another structure
query = Chem.MolFromSmarts('c1ccccc1')  # Benzene
replacement = Chem.MolFromSmiles('C1CCCCC1')  # Cyclohexane

new_mol = Chem.ReplaceSubstructs(mol, query, replacement)[0]
```

**Neutralizing Charges:**

```python
# Remove formal charges by adding/removing hydrogens
from rdkit.Chem.MolStandardize import rdMolStandardize

# Using Uncharger
uncharger = rdMolStandardize.Uncharger()
mol_neutral = uncharger.uncharge(mol)
```

### 11. Working with Molecular Hashes and Standardization

**Molecular Hashing:**

```python
from rdkit.Chem import rdMolHash

# Generate Murcko scaffold hash
scaffold_hash = rdMolHash.MolHash(mol, rdMolHash.HashFunction.MurckoScaffold)

# Canonical SMILES hash
canonical_hash = rdMolHash.MolHash(mol, rdMolHash.HashFunction.CanonicalSmiles)

# Regioisomer hash (ignores stereochemistry)
regio_hash = rdMolHash.MolHash(mol, rdMolHash.HashFunction.Regioisomer)
```

**Randomized SMILES:**

```python
# Generate random SMILES representations (for data augmentation)
from rdkit.Chem import MolToRandomSmilesVect

random_smiles = MolToRandomSmilesVect(mol, numSmiles=10, randomSeed=42)
```

### 12. Pharmacophore and 3D Features

**Pharmacophore Features:**

```python
from rdkit.Chem import ChemicalFeatures
from rdkit import RDConfig
import os

# Load feature factory
fdef_path = os.path.join(RDConfig.RDDataDir, 'BaseFeatures.fdef')
factory = ChemicalFeatures.BuildFeatureFactory(fdef_path)

# Get pharmacophore features
features = factory.GetFeaturesForMol(mol)

for feat in features:
    print(feat.GetFamily(), feat.GetType(), feat.GetAtomIds())
```

## Common Workflows

### Drug-likeness Analysis

```python
from rdkit import Chem
from rdkit.Chem import Descriptors

def analyze_druglikeness(smiles):
    mol = Chem.MolFromSmiles(smiles)
    if mol is None:
        return None

    # Calculate Lipinski descriptors
    results = {
        'MW': Descriptors.MolWt(mol),
        'LogP': Descriptors.MolLogP(mol),
        'HBD': Descriptors.NumHDonors(mol),
        'HBA': Descriptors.NumHAcceptors(mol),
        'TPSA': Descriptors.TPSA(mol),
        'RotBonds': Descriptors.NumRotatableBonds(mol)
    }

    # Check Lipinski's Rule of Five
    results['Lipinski'] = (
        results['MW'] <= 500 and
        results['LogP'] <= 5 and
        results['HBD'] <= 5 and
        results['HBA'] <= 10
    )

    return results
```

### Similarity Screening

```python
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit import DataStructs

def similarity_screen(query_smiles, database_smiles, threshold=0.7):
    query_mol = Chem.MolFromSmiles(query_smiles)
    query_fp = AllChem.GetMorganFingerprintAsBitVect(query_mol, 2)

    hits = []
    for idx, smiles in enumerate(database_smiles):
        mol = Chem.MolFromSmiles(smiles)
        if mol:
            fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2)
            sim = DataStructs.TanimotoSimilarity(query_fp, fp)
            if sim >= threshold:
                hits.append((idx, smiles, sim))

    return sorted(hits, key=lambda x: x[2], reverse=True)
```

### Substructure Filtering

```python
from rdkit import Chem

def filter_by_substructure(smiles_list, pattern_smarts):
    query = Chem.MolFromSmarts(pattern_smarts)

    hits = []
    for smiles in smiles_list:
        mol = Chem.MolFromSmiles(smiles)
        if mol and mol.HasSubstructMatch(query):
            hits.append(smiles)

    return hits
```

## Best Practices

### Error Handling

Always check for `None` when parsing molecules:

```python
mol = Chem.MolFromSmiles(smiles)
if mol is None:
    print(f"Failed to parse: {smiles}")
    continue
```

### Performance Optimization

**Use binary formats for storage:**

```python
import pickle

# Pickle molecules for fast loading
with open('molecules.pkl', 'wb') as f:
    pickle.dump(mols, f)

# Load pickled molecules (much faster than reparsing)
with open('molecules.pkl', 'rb') as f:
    mols = pickle.load(f)
```

**Use bulk operations:**

```python
# Calculate fingerprints for all molecules at once
fps = [AllChem.GetMorganFingerprintAsBitVect(mol, 2) for mol in mols]

# Use bulk similarity calculations
similarities = DataStructs.BulkTanimotoSimilarity(fps[0], fps[1:])
```

### Thread Safety

RDKit operations are generally thread-safe for:
- Molecule I/O (SMILES, mol blocks)
- Coordinate generation
- Fingerprinting and descriptors
- Substructure searching
- Reactions
- Drawing

**Not thread-safe:** MolSuppliers when accessed concurrently.

### Memory Management

For large datasets:

```python
# Use ForwardSDMolSupplier to avoid loading entire file
with open('large.sdf') as f:
    suppl = Chem.ForwardSDMolSupplier(f)
    for mol in suppl:
        # Process one molecule at a time
        pass

# Use MultithreadedSDMolSupplier for parallel processing
suppl = Chem.MultithreadedSDMolSupplier('large.sdf', numWriterThreads=4)
```

## Common Pitfalls

1. **Forgetting to check for None:** Always validate molecules after parsing
2. **Sanitization failures:** Use `DetectChemistryProblems()` to debug
3. **Missing hydrogens:** Use `AddHs()` when calculating properties that depend on hydrogen
4. **2D vs 3D:** Generate appropriate coordinates before visualization or 3D analysis
5. **SMARTS matching rules:** Remember that unspecified properties match anything
6. **Thread safety with MolSuppliers:** Don't share supplier objects across threads

## Resources

### references/

This skill includes detailed API reference documentation:

- `api_reference.md` - Comprehensive listing of RDKit modules, functions, and classes organized by functionality
- `descriptors_reference.md` - Complete list of available molecular descriptors with descriptions
- `smarts_patterns.md` - Common SMARTS patterns for functional groups and structural features

Load these references when needing specific API details, parameter information, or pattern examples.

### scripts/

Example scripts for common RDKit workflows:

- `molecular_properties.py` - Calculate comprehensive molecular properties and descriptors
- `similarity_search.py` - Perform fingerprint-based similarity screening
- `substructure_filter.py` - Filter molecules by substructure patterns

These scripts can be executed directly or used as templates for custom workflows.