RDKit Core Capabilities

The twelve capability areas in full, with worked code: molecular I/O and creation, sanitization and validation, analysis and properties, descriptors, fingerprints and similarity, substructure searching with SMARTS, chemical reactions, 2D/3D coordinate generation, visualization, molecular modification, hashes and standardization, and pharmacophore and 3D features.

Code fragments with undefined inputs such as mol1, paths, or atom IDs are illustrative templates. The checked, self-contained examples and coverage are listed in review.md.

Core Capabilities

1. Molecular I/O and Creation

Reading Molecules:

Read molecular structures from various formats:

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:

# 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:

# 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)
smiles_mols = [m for m in suppl if m is not None]  # iterate, do not use list(suppl)

# For large files or compressed data
import gzip

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:

• Tested 2026.03.6 behavior: list(SmilesMolSupplier(...)) on a fresh supplier can return an empty list because of length-hint interaction. A comprehension or ordinary iteration reads the records correctly; the bundled reader uses source lines directly.
• Multithreaded suppliers are still marked experimental upstream; they may reorder results. Save GetLastRecordId() at each iteration.
• Many molecular parse failures return None; missing files, wrong argument types, and some sanitization errors raise exceptions. Empty SMILES gives a zero-atom Mol; reject it for these workflows.
• 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, checking supported valences and assigning graph properties such as aromaticity. This is not a chemical plausibility, stability, or synthetic-feasibility proof.

Sanitization Control:

# 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)
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:

# 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 the symmetrized SSSR (can contain more rings than the minimal SSSR)
from rdkit.Chem import GetSymmSSSR
rings = GetSymmSSSR(mol)

Stereochemistry:

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

# Assign stereochemistry from existing 3D coordinates; this may overwrite tags
from rdkit.Chem import AssignStereochemistryFrom3D
AssignStereochemistryFrom3D(mol)

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

Fragment Analysis:

# 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])

# Extract Murcko scaffold (not a ring-system count)
from rdkit.Chem.Scaffolds import MurckoScaffold
scaffold = MurckoScaffold.GetScaffoldForMol(mol)

4. Molecular Descriptors and Properties

Basic Descriptors:

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:

# 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:

# 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:

from rdkit.Chem import rdFingerprintGenerator
from rdkit.Chem import MACCSkeys

# RDKit topological fingerprint
rdk_gen = rdFingerprintGenerator.GetRDKitFPGenerator(minPath=1, maxPath=7, fpSize=2048)
fp = rdk_gen.GetFingerprint(mol)

# Morgan fingerprints (circular fingerprints, similar to ECFP)
# Modern API using rdFingerprintGenerator
morgan_gen = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)
fp = morgan_gen.GetFingerprint(mol)
# Count-based fingerprint
fp_count = morgan_gen.GetCountFingerprint(mol)

# MACCS keys: 167-bit vector, 166 keys and unused bit 0
fp = MACCSkeys.GenMACCSKeys(mol)

# Atom pair fingerprints
ap_gen = rdFingerprintGenerator.GetAtomPairGenerator()
fp = ap_gen.GetFingerprint(mol)

# Topological torsion fingerprints
tt_gen = rdFingerprintGenerator.GetTopologicalTorsionGenerator()
fp = tt_gen.GetFingerprint(mol)

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

Similarity Calculation:

from rdkit import DataStructs
from rdkit.Chem import rdFingerprintGenerator

# Generate fingerprints using generator
mfpgen = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)
fp1 = mfpgen.GetFingerprint(mol1)
fp2 = mfpgen.GetFingerprint(mol2)

# Calculate Tanimoto similarity
similarity = DataStructs.TanimotoSimilarity(fp1, fp2)

# Calculate similarity for multiple molecules
fps = [mfpgen.GetFingerprint(m) for m in [mol2, mol3, mol4]]
similarities = DataStructs.BulkTanimotoSimilarity(fp1, fps)

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

Clustering and Diversity:

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

# Calculate distance matrix
dists = []
mfpgen = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)
fps = [mfpgen.GetFingerprint(mol) 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:

# 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:

# 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

# Atoms whose smallest ring has at least 12 atoms
macrocycle = Chem.MolFromSmarts('[r{12-}]')

Matching Rules:

• Unspecified properties in query match any value in target
• Hydrogen count, explicit H atoms, aromaticity, and charge are query-specific. Stereo is ignored by default; set useChirality=True for specified query stereo.
• 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:

from rdkit.Chem import AllChem

# Define reaction using SMARTS: reactants >> products
rxn = AllChem.ReactionFromSmarts('[C:1]=[O:2]>>[C:1][O:2]')  # Carbonyl bond-order transform

# 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
• Stereo handling depends on mapped atoms and reaction stereo annotations; test representative stereoisomers. Products are unsanitized and may duplicate across matches. RunReactants(..., maxProducts=1000) defaults to a cap; hitting it may mean truncation. A graph transform does not predict reagents, yield, feasibility, selectivity, or a balanced experimental reaction.

Reaction Similarity:

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

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

8. 2D and 3D Coordinate Generation

2D Coordinate Generation:

from rdkit.Chem import AllChem

# Depict a copy: Compute2DCoords clears existing conformers by default
depiction = Chem.Mol(mol)
AllChem.Compute2DCoords(depiction)

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

3D Coordinate Generation and Conformers:

from rdkit import Chem
from rdkit.Chem import AllChem

mol3d = Chem.AddHs(Chem.MolFromSmiles('CCCO'))
params = AllChem.ETKDGv3()
params.randomSeed = 42
params.numThreads = 1
params.trackFailures = True
conf_ids = list(AllChem.EmbedMultipleConfs(mol3d, numConfs=5, params=params))
if not conf_ids:
    raise RuntimeError(f"Embedding failed: {params.GetFailureCounts()}")
if not AllChem.MMFFHasAllMoleculeParams(mol3d):
    raise ValueError("MMFF parameters unavailable for this chemistry")
results = AllChem.MMFFOptimizeMoleculeConfs(mol3d, numThreads=1, maxIters=500)
# Conformer IDs are identifiers, not necessarily dense list positions.
energies = {cid: energy for cid, (status, energy) in zip(conf_ids, results)
            if status == 0}
if not energies:
    raise RuntimeError("No converged MMFF conformer")
best_id = min(energies, key=energies.get)
print(best_id, energies[best_id])  # energy in kcal/mol; coordinates in angstrom

Single-conformer optimization returns 0 for convergence, 1 for an iteration limit; MMFF additionally returns -1 if setup fails. UFF needs its own UFFHasAllMoleculeParams check. Do not silently switch force fields and compare energies. These are local force-field energies, not free energies, conformer populations, binding affinities, or guarantees of global minima. For RMSD, AllChem.GetConformerRMS(mol, id1, id2) aligns and mutates conformers by default; use a copy when retaining original coordinates. RMSD is in angstrom and depends on atom mapping, H inclusion, and symmetry treatment.

Constrained Embedding:

# Embed with part of molecule constrained to specific coordinates
embedded = AllChem.ConstrainedEmbed(mol, core_mol)  # core must have 3D coordinates

9. Molecular Visualization

Basic Drawing:

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:

# 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:

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:

# 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:

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

# For Morgan fingerprints
additional_output = rdFingerprintGenerator.AdditionalOutput()
additional_output.AllocateBitInfoMap()
morgan_gen = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)
fp = morgan_gen.GetFingerprint(mol, additionalOutput=additional_output)
bit_info = additional_output.GetBitInfoMap()

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

10. Molecular Modification

Adding/Removing Hydrogens:

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

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

Kekulization and Aromaticity:

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

# Set aromaticity
Chem.SetAromaticity(mol)

Replacing Substructures:

# 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:

# 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:

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 abstraction (not a general stereo-removal operation)
regio_hash = rdMolHash.MolHash(mol, rdMolHash.HashFunction.Regioisomer)

Randomized SMILES:

# 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:

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())