What it does. Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms. Part of K-Dense-AI/scientific-agent-skills (AI Scientist skills) (K-Dense-AI/scientific-agent-skills).

Upstream	K-Dense-AI/scientific-agent-skills
Skill file	skills/rdkit/SKILL.md
License	MIT
Author	K-Dense Inc.
Fetched	2026-09-10

Install

• npx skills add K-Dense-AI/scientific-agent-skills --skill rdkit, or copy the skill folder into ~/.claude/skills/rdkit/.
• Raw file: curl -sL https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/HEAD/skills/rdkit/SKILL.md

SKILL.md (verbatim)

name: rdkit
description: Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.
license: BSD-3-Clause license
allowed-tools: Read Write Edit Bash
compatibility: Examples target RDKit 2026.03.x. Use conda-forge for the broadest binary support or PyPI package `rdkit` for supported platform wheels; `rdkit-pypi` is the legacy PyPI name.
metadata:
  version: "1.3"
  skill-author: K-Dense Inc.

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.

Current baseline (checked 2026-06-07): RDKit 2026.03.3 is the latest GitHub/PyPI release (rdkit 2026.3.3 on PyPI). Official installation docs continue to recommend conda-forge for most users, while cross-platform PyPI wheels are published under the rdkit package name. rdkit-pypi is the old PyPI package name and should only appear when maintaining legacy environments.

Installation and Setup

Use uv when installing into an existing Python environment:

uv pip install rdkit

For reproducible chemistry environments, especially when mixing compiled scientific packages, conda-forge remains the upstream recommendation:

conda create -c conda-forge -n my-rdkit-env rdkit
conda activate my-rdkit-env

Avoid installing both conda rdkit and PyPI rdkit/rdkit-pypi into the same environment unless you are deliberately debugging packaging behavior. Mixed installs can make it unclear which binary extension is being imported.

Core Capabilities

Twelve capability areas, each with worked code, are documented in references/core_capabilities.md:

#	Area	Covers
1	Molecular I/O and creation	SMILES, MOL files and blocks, InChI, SDF and SMILES suppliers, multithreaded reading, writers
2	Sanitization and validation	disabling automatic sanitization, manual and partial sanitization, detecting problems first
3	Analysis and properties	atom and bond iteration, ring information and SSSR, chirality and stereochemistry, fragments
4	Descriptors	MW, LogP, TPSA, H-bond donors/acceptors, rotatable bonds, aromatic rings, bulk calculation, drug-likeness
5	Fingerprints and similarity	topological, Morgan/ECFP via rdFingerprintGenerator, MACCS, atom pair, torsion, Avalon; Tanimoto and other metrics; Butina clustering
6	Substructure searching	SMARTS queries, match retrieval, and a library of common patterns
7	Chemical reactions	reaction SMARTS, applying reactions, reaction fingerprints
8	2D and 3D coordinates	depiction, template alignment, ETKDG embedding, force-field optimization, RMSD, constrained embedding
9	Visualization	single and grid images, substructure highlighting, custom drawer options, Jupyter integration, fingerprint bit environments
10	Molecular modification	explicit hydrogens, Kekulization, aromaticity, substructure replacement, charge neutralization
11	Hashes and standardization	Murcko scaffold and canonical hashes, regioisomer hashes, randomized SMILES for augmentation
12	Pharmacophore and 3D features	feature factories and feature extraction

Worked workflows and the performance, thread-safety, and version-sensitivity notes are in references/workflows_and_best_practices.md.

Prefer portable exchange formats (SMILES, SDF) for shared data; for local caches RDKit's binary molecule representation avoids generic pickle.

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.

Only the files listed in references/ and scripts/ are bundled local resources. Names such as rdkit, datamol, scipy, and sklearn refer to installable Python packages, not local files in this skill.

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.

Citing Scientific Agent Skills

This skill is part of Scientific Agent Skills by K-Dense. If it materially contributed to a manuscript, report, presentation, or code release, add the paper to the references or software section and tell the user you did so:

Kassis, T., Agarwal, V., He, Y., Patel, D., & Brueckner, A. M. (2026). Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents. arXiv:2609.00065. https://doi.org/10.48550/arXiv.2609.00065

Always cite the current version. The DOI and https://arxiv.org/abs/2609.00065 resolve to the latest arXiv version, so never append a version suffix such as v1. When network access is available, fetch https://arxiv.org/abs/2609.00065 (or http://export.arxiv.org/api/query?id_list=2609.00065) before writing the reference and take the author list, year, and version from that record. If the record lists a journal reference or publisher DOI, cite the published version instead.

Other files in this skill

• references/api_reference.md
• references/core_capabilities.md
• references/descriptors_reference.md
• references/smarts_patterns.md
• references/workflows_and_best_practices.md
• scripts/molecular_properties.py
• scripts/similarity_search.py
• scripts/substructure_filter.py

references/api_reference.md (verbatim)

RDKit API Reference

This document provides a comprehensive reference for RDKit's Python API, organized by functionality.

Core Module: rdkit.Chem

The fundamental module for working with molecules.

Molecule I/O

Reading Molecules:

• Chem.MolFromSmiles(smiles, sanitize=True) - Parse SMILES string
• Chem.MolFromSmarts(smarts) - Parse SMARTS pattern
• Chem.MolFromMolFile(filename, sanitize=True, removeHs=True) - Read MOL file
• Chem.MolFromMolBlock(molblock, sanitize=True, removeHs=True) - Parse MOL block string
• Chem.MolFromMol2File(filename, sanitize=True, removeHs=True) - Read MOL2 file
• Chem.MolFromMol2Block(molblock, sanitize=True, removeHs=True) - Parse MOL2 block
• Chem.MolFromPDBFile(filename, sanitize=True, removeHs=True) - Read PDB file
• Chem.MolFromPDBBlock(pdbblock, sanitize=True, removeHs=True) - Parse PDB block
• Chem.MolFromInchi(inchi, sanitize=True, removeHs=True) - Parse InChI string
• Chem.MolFromSequence(seq, sanitize=True) - Create molecule from peptide sequence

Writing Molecules:

• Chem.MolToSmiles(mol, isomericSmiles=True, canonical=True) - Convert to SMILES
• Chem.MolToSmarts(mol, isomericSmiles=False) - Convert to SMARTS
• Chem.MolToMolBlock(mol, includeStereo=True, confId=-1) - Convert to MOL block
• Chem.MolToMolFile(mol, filename, includeStereo=True, confId=-1) - Write MOL file
• Chem.MolToPDBBlock(mol, confId=-1) - Convert to PDB block
• Chem.MolToPDBFile(mol, filename, confId=-1) - Write PDB file
• Chem.MolToInchi(mol, options='') - Convert to InChI
• Chem.MolToInchiKey(mol, options='') - Generate InChI key
• Chem.MolToSequence(mol) - Convert to peptide sequence

Batch I/O:

• Chem.SDMolSupplier(filename, sanitize=True, removeHs=True) - SDF file reader
• Chem.ForwardSDMolSupplier(fileobj, sanitize=True, removeHs=True) - Forward-only SDF reader
• Chem.MultithreadedSDMolSupplier(filename, numWriterThreads=1) - Parallel SDF reader
• Chem.SmilesMolSupplier(filename, delimiter=' ', titleLine=True) - SMILES file reader
• Chem.SDWriter(filename) - SDF file writer
• Chem.SmilesWriter(filename, delimiter=' ', includeHeader=True) - SMILES file writer

Molecular Manipulation

Sanitization:

• Chem.SanitizeMol(mol, sanitizeOps=SANITIZE_ALL, catchErrors=False) - Sanitize molecule
• Chem.DetectChemistryProblems(mol, sanitizeOps=SANITIZE_ALL) - Detect sanitization issues
• Chem.AssignStereochemistry(mol, cleanIt=True, force=False) - Assign stereochemistry
• Chem.FindPotentialStereo(mol) - Find potential stereocenters
• Chem.AssignStereochemistryFrom3D(mol, confId=-1) - Assign stereo from 3D coords

Hydrogen Management:

• Chem.AddHs(mol, explicitOnly=False, addCoords=False) - Add explicit hydrogens
• Chem.RemoveHs(mol, implicitOnly=False, updateExplicitCount=False) - Remove hydrogens
• Chem.RemoveAllHs(mol) - Remove all hydrogens

Aromaticity:

• Chem.SetAromaticity(mol, model=AROMATICITY_RDKIT) - Set aromaticity model
• Chem.Kekulize(mol, clearAromaticFlags=False) - Kekulize aromatic bonds
• Chem.SetConjugation(mol) - Set conjugation flags

Fragments:

• Chem.GetMolFrags(mol, asMols=False, sanitizeFrags=True) - Get disconnected fragments
• Chem.FragmentOnBonds(mol, bondIndices, addDummies=True) - Fragment on specific bonds
• Chem.ReplaceSubstructs(mol, query, replacement, replaceAll=False) - Replace substructures
• Chem.DeleteSubstructs(mol, query, onlyFrags=False) - Delete substructures

Stereochemistry:

• Chem.FindMolChiralCenters(mol, includeUnassigned=False, useLegacyImplementation=False) - Find chiral centers
• Chem.FindPotentialStereo(mol, cleanIt=True) - Find potential stereocenters

Substructure Searching

Basic Matching:

• mol.HasSubstructMatch(query, useChirality=False) - Check for substructure match
• mol.GetSubstructMatch(query, useChirality=False) - Get first match
• mol.GetSubstructMatches(query, uniquify=True, useChirality=False) - Get all matches
• mol.GetSubstructMatches(query, maxMatches=1000) - Limit number of matches

Molecular Properties

Atom Methods:

• atom.GetSymbol() - Atomic symbol
• atom.GetAtomicNum() - Atomic number
• atom.GetDegree() - Number of bonds
• atom.GetTotalDegree() - Including hydrogens
• atom.GetFormalCharge() - Formal charge
• atom.GetNumRadicalElectrons() - Radical electrons
• atom.GetIsAromatic() - Aromaticity flag
• atom.GetHybridization() - Hybridization (SP, SP2, SP3, etc.)
• atom.GetIdx() - Atom index
• atom.IsInRing() - In any ring
• atom.IsInRingSize(size) - In ring of specific size
• atom.GetChiralTag() - Chirality tag

Bond Methods:

• bond.GetBondType() - Bond type (SINGLE, DOUBLE, TRIPLE, AROMATIC)
• bond.GetBeginAtomIdx() - Starting atom index
• bond.GetEndAtomIdx() - Ending atom index
• bond.GetIsConjugated() - Conjugation flag
• bond.GetIsAromatic() - Aromaticity flag
• bond.IsInRing() - In any ring
• bond.GetStereo() - Stereochemistry (STEREONONE, STEREOZ, STEREOE, etc.)

Molecule Methods:

• mol.GetNumAtoms(onlyExplicit=True) - Number of atoms
• mol.GetNumHeavyAtoms() - Number of heavy atoms
• mol.GetNumBonds() - Number of bonds
• mol.GetAtoms() - Iterator over atoms
• mol.GetBonds() - Iterator over bonds
• mol.GetAtomWithIdx(idx) - Get specific atom
• mol.GetBondWithIdx(idx) - Get specific bond
• mol.GetRingInfo() - Ring information object

Ring Information:

• Chem.GetSymmSSSR(mol) - Get smallest set of smallest rings
• Chem.GetSSSR(mol) - Alias for GetSymmSSSR
• ring_info.NumRings() - Number of rings
• ring_info.AtomRings() - Tuples of atom indices in rings
• ring_info.BondRings() - Tuples of bond indices in rings

rdkit.Chem.AllChem

Extended chemistry functionality.

2D/3D Coordinate Generation

• AllChem.Compute2DCoords(mol, canonOrient=True, clearConfs=True) - Generate 2D coordinates
• AllChem.EmbedMolecule(mol, maxAttempts=0, randomSeed=-1, useRandomCoords=False) - Generate 3D conformer
• AllChem.EmbedMultipleConfs(mol, numConfs=10, maxAttempts=0, randomSeed=-1) - Generate multiple conformers
• AllChem.ConstrainedEmbed(mol, core, useTethers=True) - Constrained embedding
• AllChem.GenerateDepictionMatching2DStructure(mol, reference, refPattern=None) - Align to template

Force Field Optimization

• AllChem.UFFOptimizeMolecule(mol, maxIters=200, confId=-1) - UFF optimization
• AllChem.MMFFOptimizeMolecule(mol, maxIters=200, confId=-1, mmffVariant='MMFF94') - MMFF optimization
• AllChem.UFFGetMoleculeForceField(mol, confId=-1) - Get UFF force field object
• AllChem.MMFFGetMoleculeForceField(mol, pyMMFFMolProperties, confId=-1) - Get MMFF force field

Conformer Analysis

• AllChem.GetConformerRMS(mol, confId1, confId2, prealigned=False) - Calculate RMSD
• AllChem.GetConformerRMSMatrix(mol, prealigned=False) - RMSD matrix
• AllChem.AlignMol(prbMol, refMol, prbCid=-1, refCid=-1) - Align molecules
• AllChem.AlignMolConformers(mol) - Align all conformers

Reactions

• AllChem.ReactionFromSmarts(smarts, useSmiles=False) - Create reaction from SMARTS
• reaction.RunReactants(reactants) - Apply reaction
• reaction.RunReactant(reactant, reactionIdx) - Apply to specific reactant
• AllChem.CreateDifferenceFingerprintForReaction(reaction) - Reaction fingerprint

Fingerprints

• Prefer rdFingerprintGenerator.GetMorganGenerator() for new Morgan fingerprint code
• AllChem.GetMorganFingerprint(mol, radius, useFeatures=False) - Legacy Morgan fingerprint helper
• AllChem.GetMorganFingerprintAsBitVect(mol, radius, nBits=2048) - Legacy Morgan bit vector helper
• AllChem.GetHashedMorganFingerprint(mol, radius, nBits=2048) - Legacy hashed Morgan helper
• AllChem.GetErGFingerprint(mol) - ErG fingerprint

rdkit.Chem.Descriptors

Molecular descriptor calculations.

Common Descriptors

• Descriptors.MolWt(mol) - Molecular weight
• Descriptors.ExactMolWt(mol) - Exact molecular weight
• Descriptors.HeavyAtomMolWt(mol) - Heavy atom molecular weight
• Descriptors.MolLogP(mol) - LogP (lipophilicity)
• Descriptors.MolMR(mol) - Molar refractivity
• Descriptors.TPSA(mol) - Topological polar surface area
• Descriptors.NumHDonors(mol) - Hydrogen bond donors
• Descriptors.NumHAcceptors(mol) - Hydrogen bond acceptors
• Descriptors.NumRotatableBonds(mol) - Rotatable bonds
• Descriptors.NumAromaticRings(mol) - Aromatic rings
• Descriptors.NumSaturatedRings(mol) - Saturated rings
• Descriptors.NumAliphaticRings(mol) - Aliphatic rings
• Descriptors.NumAromaticHeterocycles(mol) - Aromatic heterocycles
• Descriptors.NumRadicalElectrons(mol) - Radical electrons
• Descriptors.NumValenceElectrons(mol) - Valence electrons

Batch Calculation

• Descriptors.CalcMolDescriptors(mol) - Calculate all descriptors as dictionary

Descriptor Lists

• Descriptors._descList - List of (name, function) tuples for all descriptors

rdkit.Chem.Draw

Molecular visualization.

Image Generation

• Draw.MolToImage(mol, size=(300,300), kekulize=True, wedgeBonds=True, highlightAtoms=None) - Generate PIL image
• Draw.MolToFile(mol, filename, size=(300,300), kekulize=True, wedgeBonds=True) - Save to file
• Draw.MolsToGridImage(mols, molsPerRow=3, subImgSize=(200,200), legends=None) - Grid of molecules
• Draw.MolsMatrixToGridImage(mols, molsPerRow=3, subImgSize=(200,200), legends=None) - Nested grid
• Draw.ReactionToImage(rxn, subImgSize=(200,200)) - Reaction image

Fingerprint Visualization

• Draw.DrawMorganBit(mol, bitId, bitInfo, whichExample=0) - Visualize Morgan bit
• Draw.DrawMorganBits(bits, mol, bitInfo, molsPerRow=3) - Multiple Morgan bits
• Draw.DrawRDKitBit(mol, bitId, bitInfo, whichExample=0) - Visualize RDKit bit

IPython Integration

• Draw.IPythonConsole - Module for Jupyter integration
• Draw.IPythonConsole.ipython_useSVG - Use SVG (True) or PNG (False)
• Draw.IPythonConsole.molSize - Default molecule image size

Drawing Options

• rdMolDraw2D.MolDrawOptions() - Get drawing options object
  • .addAtomIndices - Show atom indices
  • .addBondIndices - Show bond indices
  • .addStereoAnnotation - Show stereochemistry
  • .bondLineWidth - Line width
  • .highlightBondWidthMultiplier - Highlight width
  • .minFontSize - Minimum font size
  • .maxFontSize - Maximum font size

rdkit.Chem.rdMolDescriptors

Additional descriptor calculations.

• rdMolDescriptors.CalcNumRings(mol) - Number of rings
• rdMolDescriptors.CalcNumAromaticRings(mol) - Aromatic rings
• rdMolDescriptors.CalcNumAliphaticRings(mol) - Aliphatic rings
• rdMolDescriptors.CalcNumSaturatedRings(mol) - Saturated rings
• rdMolDescriptors.CalcNumHeterocycles(mol) - Heterocycles
• rdMolDescriptors.CalcNumAromaticHeterocycles(mol) - Aromatic heterocycles
• rdMolDescriptors.CalcNumSpiroAtoms(mol) - Spiro atoms
• rdMolDescriptors.CalcNumBridgeheadAtoms(mol) - Bridgehead atoms
• rdMolDescriptors.CalcFractionCsp3(mol) - Fraction of sp3 carbons
• rdMolDescriptors.CalcLabuteASA(mol) - Labute accessible surface area
• rdMolDescriptors.CalcTPSA(mol) - TPSA
• rdMolDescriptors.CalcMolFormula(mol) - Molecular formula

rdkit.Chem.Scaffolds

Scaffold analysis.

Murcko Scaffolds

• MurckoScaffold.GetScaffoldForMol(mol) - Get Murcko scaffold
• MurckoScaffold.MakeScaffoldGeneric(mol) - Generic scaffold
• MurckoScaffold.MurckoDecompose(mol) - Decompose to scaffold and sidechains

rdkit.Chem.rdMolHash

Molecular hashing and standardization.

• rdMolHash.MolHash(mol, hashFunction) - Generate hash
  • rdMolHash.HashFunction.AnonymousGraph - Anonymized structure
  • rdMolHash.HashFunction.CanonicalSmiles - Canonical SMILES
  • rdMolHash.HashFunction.ElementGraph - Element graph
  • rdMolHash.HashFunction.MurckoScaffold - Murcko scaffold
  • rdMolHash.HashFunction.Regioisomer - Regioisomer (no stereo)
  • rdMolHash.HashFunction.NetCharge - Net charge
  • rdMolHash.HashFunction.HetAtomProtomer - Heteroatom protomer
  • rdMolHash.HashFunction.HetAtomTautomer - Heteroatom tautomer

rdkit.Chem.MolStandardize

Molecule standardization.

Import the current standardization implementation from rdkit.Chem.MolStandardize:

from rdkit.Chem.MolStandardize import rdMolStandardize

• rdMolStandardize.Normalize(mol) - Normalize functional groups
• rdMolStandardize.Reionize(mol) - Fix ionization state
• rdMolStandardize.RemoveFragments(mol) - Remove small fragments
• rdMolStandardize.Cleanup(mol) - Full cleanup (normalize + reionize + remove)
• rdMolStandardize.Uncharger() - Create uncharger object
  • .uncharge(mol) - Remove charges
• rdMolStandardize.TautomerEnumerator() - Enumerate tautomers
  • .Enumerate(mol) - Generate tautomers
  • .Canonicalize(mol) - Get canonical tautomer

rdkit.DataStructs

Fingerprint similarity and operations.

Similarity Metrics

• DataStructs.TanimotoSimilarity(fp1, fp2) - Tanimoto coefficient
• DataStructs.DiceSimilarity(fp1, fp2) - Dice coefficient
• DataStructs.CosineSimilarity(fp1, fp2) - Cosine similarity
• DataStructs.SokalSimilarity(fp1, fp2) - Sokal similarity
• DataStructs.KulczynskiSimilarity(fp1, fp2) - Kulczynski similarity
• DataStructs.McConnaugheySimilarity(fp1, fp2) - McConnaughey similarity

Bulk Operations

• DataStructs.BulkTanimotoSimilarity(fp, fps) - Tanimoto for list of fingerprints
• DataStructs.BulkDiceSimilarity(fp, fps) - Dice for list
• DataStructs.BulkCosineSimilarity(fp, fps) - Cosine for list

Distance Metrics

• DataStructs.TanimotoDistance(fp1, fp2) - 1 - Tanimoto
• DataStructs.DiceDistance(fp1, fp2) - 1 - Dice

rdkit.Chem.AtomPairs

Atom pair fingerprints.

• Pairs.GetAtomPairFingerprint(mol, minLength=1, maxLength=30) - Atom pair fingerprint
• Pairs.GetAtomPairFingerprintAsBitVect(mol, minLength=1, maxLength=30, nBits=2048) - As bit vector
• Pairs.GetHashedAtomPairFingerprint(mol, nBits=2048, minLength=1, maxLength=30) - Hashed version

rdkit.Chem.Torsions

Topological torsion fingerprints.

• Torsions.GetTopologicalTorsionFingerprint(mol, targetSize=4) - Torsion fingerprint
• Torsions.GetTopologicalTorsionFingerprintAsIntVect(mol, targetSize=4) - As int vector
• Torsions.GetHashedTopologicalTorsionFingerprint(mol, nBits=2048, targetSize=4) - Hashed version

rdkit.Chem.MACCSkeys

MACCS structural keys.

• MACCSkeys.GenMACCSKeys(mol) - Generate 166-bit MACCS keys

rdkit.Chem.ChemicalFeatures

Pharmacophore features.

• ChemicalFeatures.BuildFeatureFactory(featureFile) - Create feature factory
• factory.GetFeaturesForMol(mol) - Get pharmacophore features
• feature.GetFamily() - Feature family (Donor, Acceptor, etc.)
• feature.GetType() - Feature type
• feature.GetAtomIds() - Atoms involved in feature

rdkit.ML.Cluster.Butina

Clustering algorithms.

• Butina.ClusterData(distances, nPts, distThresh, isDistData=True) - Butina clustering
  • Returns tuple of tuples with cluster members

rdkit.Chem.rdFingerprintGenerator

Modern fingerprint generation API. Prefer this over legacy AllChem.GetMorganFingerprint* helpers for new code.

• rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048) - Morgan generator
• rdFingerprintGenerator.GetRDKitFPGenerator(minPath=1, maxPath=7, fpSize=2048) - RDKit FP generator
• rdFingerprintGenerator.GetAtomPairGenerator(minDistance=1, maxDistance=30) - Atom pair generator
• rdFingerprintGenerator.GetTopologicalTorsionGenerator(fpSize=2048) - Topological torsion generator
• generator.GetFingerprint(mol) - Generate fingerprint
• generator.GetCountFingerprint(mol) - Count-based fingerprint
• rdFingerprintGenerator.AdditionalOutput() - Collect bit information for visualization and explanations

Common Parameters

Sanitization Operations

• SANITIZE_NONE - No sanitization
• SANITIZE_ALL - All operations (default)
• SANITIZE_CLEANUP - Basic cleanup
• SANITIZE_PROPERTIES - Calculate properties
• SANITIZE_SYMMRINGS - Symmetrize rings
• SANITIZE_KEKULIZE - Kekulize aromatic rings
• SANITIZE_FINDRADICALS - Find radical electrons
• SANITIZE_SETAROMATICITY - Set aromaticity
• SANITIZE_SETCONJUGATION - Set conjugation
• SANITIZE_SETHYBRIDIZATION - Set hybridization
• SANITIZE_CLEANUPCHIRALITY - Cleanup chirality

Bond Types

• BondType.SINGLE - Single bond
• BondType.DOUBLE - Double bond
• BondType.TRIPLE - Triple bond
• BondType.AROMATIC - Aromatic bond
• BondType.DATIVE - Dative bond
• BondType.UNSPECIFIED - Unspecified

Hybridization

• HybridizationType.S - S
• HybridizationType.SP - SP
• HybridizationType.SP2 - SP2
• HybridizationType.SP3 - SP3
• HybridizationType.SP3D - SP3D
• HybridizationType.SP3D2 - SP3D2

Chirality

• ChiralType.CHI_UNSPECIFIED - Unspecified
• ChiralType.CHI_TETRAHEDRAL_CW - Clockwise
• ChiralType.CHI_TETRAHEDRAL_CCW - Counter-clockwise

Installation

# Existing uv/pip environment
uv pip install rdkit

# Fresh conda-forge environment (upstream recommendation)
conda create -c conda-forge -n my-rdkit-env rdkit

The PyPI package is now rdkit; rdkit-pypi is the legacy name for older releases.

Importing

# Core functionality
from rdkit import Chem
from rdkit.Chem import AllChem

# Descriptors
from rdkit.Chem import Descriptors

# Drawing
from rdkit.Chem import Draw

# Similarity
from rdkit import DataStructs

references/core_capabilities.md (verbatim)

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.

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)

# 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:

• 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:

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

Stereochemistry:

# 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:

# 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:

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 (166-bit structural key)
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

# 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:

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:

# 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

# 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:

# 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:

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

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 hash (ignores stereochemistry)
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())

references/descriptors_reference.md (verbatim)

RDKit Molecular Descriptors Reference

Complete reference for molecular descriptors available in RDKit's Descriptors module.

Usage

from rdkit import Chem
from rdkit.Chem import Descriptors

mol = Chem.MolFromSmiles('CCO')

# Calculate individual descriptor
mw = Descriptors.MolWt(mol)

# Calculate all descriptors at once
all_desc = Descriptors.CalcMolDescriptors(mol)

Molecular Weight and Mass

MolWt

Average molecular weight of the molecule.

Descriptors.MolWt(mol)

ExactMolWt

Exact molecular weight using isotopic composition.

Descriptors.ExactMolWt(mol)

HeavyAtomMolWt

Average molecular weight ignoring hydrogens.

Descriptors.HeavyAtomMolWt(mol)

Lipophilicity

MolLogP

Wildman-Crippen LogP (octanol-water partition coefficient).

Descriptors.MolLogP(mol)

MolMR

Wildman-Crippen molar refractivity.

Descriptors.MolMR(mol)

Polar Surface Area

TPSA

Topological polar surface area (TPSA) based on fragment contributions.

Descriptors.TPSA(mol)

LabuteASA

Labute's Approximate Surface Area (ASA).

Descriptors.LabuteASA(mol)

Hydrogen Bonding

NumHDonors

Number of hydrogen bond donors (N-H and O-H).

Descriptors.NumHDonors(mol)

NumHAcceptors

Number of hydrogen bond acceptors (N and O).

Descriptors.NumHAcceptors(mol)

NOCount

Number of N and O atoms.

Descriptors.NOCount(mol)

NHOHCount

Number of N-H and O-H bonds.

Descriptors.NHOHCount(mol)

Atom Counts

HeavyAtomCount

Number of heavy atoms (non-hydrogen).

Descriptors.HeavyAtomCount(mol)

NumHeteroatoms

Number of heteroatoms (non-C and non-H).

Descriptors.NumHeteroatoms(mol)

NumValenceElectrons

Total number of valence electrons.

Descriptors.NumValenceElectrons(mol)

NumRadicalElectrons

Number of radical electrons.

Descriptors.NumRadicalElectrons(mol)

Ring Descriptors

RingCount

Number of rings.

Descriptors.RingCount(mol)

NumAromaticRings

Number of aromatic rings.

Descriptors.NumAromaticRings(mol)

NumSaturatedRings

Number of saturated rings.

Descriptors.NumSaturatedRings(mol)

NumAliphaticRings

Number of aliphatic (non-aromatic) rings.

Descriptors.NumAliphaticRings(mol)

NumAromaticCarbocycles

Number of aromatic carbocycles (rings with only carbons).

Descriptors.NumAromaticCarbocycles(mol)

NumAromaticHeterocycles

Number of aromatic heterocycles (rings with heteroatoms).

Descriptors.NumAromaticHeterocycles(mol)

NumSaturatedCarbocycles

Number of saturated carbocycles.

Descriptors.NumSaturatedCarbocycles(mol)

NumSaturatedHeterocycles

Number of saturated heterocycles.

Descriptors.NumSaturatedHeterocycles(mol)

NumAliphaticCarbocycles

Number of aliphatic carbocycles.

Descriptors.NumAliphaticCarbocycles(mol)

NumAliphaticHeterocycles

Number of aliphatic heterocycles.

Descriptors.NumAliphaticHeterocycles(mol)

Rotatable Bonds

NumRotatableBonds

Number of rotatable bonds (flexibility).

Descriptors.NumRotatableBonds(mol)

Aromatic Atoms

NumAromaticAtoms

Number of aromatic atoms.

Descriptors.NumAromaticAtoms(mol)

Fraction Descriptors

FractionCsp3

Fraction of carbons that are sp3 hybridized.

Descriptors.FractionCsp3(mol)

Complexity Descriptors

BertzCT

Bertz complexity index.

Descriptors.BertzCT(mol)

Ipc

Information content (complexity measure).

Descriptors.Ipc(mol)

Kappa Shape Indices

Molecular shape descriptors based on graph invariants.

Kappa1

First kappa shape index.

Descriptors.Kappa1(mol)

Kappa2

Second kappa shape index.

Descriptors.Kappa2(mol)

Kappa3

Third kappa shape index.

Descriptors.Kappa3(mol)

Chi Connectivity Indices

Molecular connectivity indices.

Chi0, Chi1, Chi2, Chi3, Chi4

Simple chi connectivity indices.

Descriptors.Chi0(mol)
Descriptors.Chi1(mol)
Descriptors.Chi2(mol)
Descriptors.Chi3(mol)
Descriptors.Chi4(mol)

Chi0n, Chi1n, Chi2n, Chi3n, Chi4n

Valence-modified chi connectivity indices.

Descriptors.Chi0n(mol)
Descriptors.Chi1n(mol)
Descriptors.Chi2n(mol)
Descriptors.Chi3n(mol)
Descriptors.Chi4n(mol)

Chi0v, Chi1v, Chi2v, Chi3v, Chi4v

Valence chi connectivity indices.

Descriptors.Chi0v(mol)
Descriptors.Chi1v(mol)
Descriptors.Chi2v(mol)
Descriptors.Chi3v(mol)
Descriptors.Chi4v(mol)

Hall-Kier Alpha

HallKierAlpha

Hall-Kier alpha value (molecular flexibility).

Descriptors.HallKierAlpha(mol)

Balaban's J Index

BalabanJ

Balaban's J index (branching descriptor).

Descriptors.BalabanJ(mol)

EState Indices

Electrotopological state indices.

MaxEStateIndex

Maximum E-state value.

Descriptors.MaxEStateIndex(mol)

MinEStateIndex

Minimum E-state value.

Descriptors.MinEStateIndex(mol)

MaxAbsEStateIndex

Maximum absolute E-state value.

Descriptors.MaxAbsEStateIndex(mol)

MinAbsEStateIndex

Minimum absolute E-state value.

Descriptors.MinAbsEStateIndex(mol)

Partial Charges

MaxPartialCharge

Maximum partial charge.

Descriptors.MaxPartialCharge(mol)

MinPartialCharge

Minimum partial charge.

Descriptors.MinPartialCharge(mol)

MaxAbsPartialCharge

Maximum absolute partial charge.

Descriptors.MaxAbsPartialCharge(mol)

MinAbsPartialCharge

Minimum absolute partial charge.

Descriptors.MinAbsPartialCharge(mol)

Fingerprint Density

Measures the density of molecular fingerprints.

FpDensityMorgan1

Morgan fingerprint density at radius 1.

Descriptors.FpDensityMorgan1(mol)

FpDensityMorgan2

Morgan fingerprint density at radius 2.

Descriptors.FpDensityMorgan2(mol)

FpDensityMorgan3

Morgan fingerprint density at radius 3.

Descriptors.FpDensityMorgan3(mol)

PEOE VSA Descriptors

Partial Equalization of Orbital Electronegativities (PEOE) VSA descriptors.

PEOE_VSA1 through PEOE_VSA14

MOE-type descriptors using partial charges and surface area contributions.

Descriptors.PEOE_VSA1(mol)
# ... through PEOE_VSA14

SMR VSA Descriptors

Molecular refractivity VSA descriptors.

SMR_VSA1 through SMR_VSA10

MOE-type descriptors using MR contributions and surface area.

Descriptors.SMR_VSA1(mol)
# ... through SMR_VSA10

SLogP VSA Descriptors

LogP VSA descriptors.

SLogP_VSA1 through SLogP_VSA12

MOE-type descriptors using LogP contributions and surface area.

Descriptors.SLogP_VSA1(mol)
# ... through SLogP_VSA12

EState VSA Descriptors

EState_VSA1 through EState_VSA11

MOE-type descriptors using E-state indices and surface area.

Descriptors.EState_VSA1(mol)
# ... through EState_VSA11

VSA Descriptors

van der Waals surface area descriptors.

VSA_EState1 through VSA_EState10

EState VSA descriptors.

Descriptors.VSA_EState1(mol)
# ... through VSA_EState10

BCUT Descriptors

Burden-CAS-University of Texas eigenvalue descriptors.

BCUT2D_MWHI

Highest eigenvalue of Burden matrix weighted by molecular weight.

Descriptors.BCUT2D_MWHI(mol)

BCUT2D_MWLOW

Lowest eigenvalue of Burden matrix weighted by molecular weight.

Descriptors.BCUT2D_MWLOW(mol)

BCUT2D_CHGHI

Highest eigenvalue weighted by partial charges.

Descriptors.BCUT2D_CHGHI(mol)

BCUT2D_CHGLO

Lowest eigenvalue weighted by partial charges.

Descriptors.BCUT2D_CHGLO(mol)

BCUT2D_LOGPHI

Highest eigenvalue weighted by LogP.

Descriptors.BCUT2D_LOGPHI(mol)

BCUT2D_LOGPLOW

Lowest eigenvalue weighted by LogP.

Descriptors.BCUT2D_LOGPLOW(mol)

BCUT2D_MRHI

Highest eigenvalue weighted by molar refractivity.

Descriptors.BCUT2D_MRHI(mol)

BCUT2D_MRLOW

Lowest eigenvalue weighted by molar refractivity.

Descriptors.BCUT2D_MRLOW(mol)

Autocorrelation Descriptors

AUTOCORR2D

2D autocorrelation descriptors (if enabled). Various autocorrelation indices measuring spatial distribution of properties.

MQN Descriptors

Molecular Quantum Numbers - 42 simple descriptors.

mqn1 through mqn42

Integer descriptors counting various molecular features.

# Access via CalcMolDescriptors
desc = Descriptors.CalcMolDescriptors(mol)
mqns = {k: v for k, v in desc.items() if k.startswith('mqn')}

QED

qed

Quantitative Estimate of Drug-likeness.

Descriptors.qed(mol)

Lipinski's Rule of Five

Check drug-likeness using Lipinski's criteria:

def lipinski_rule_of_five(mol):
    mw = Descriptors.MolWt(mol) <= 500
    logp = Descriptors.MolLogP(mol) <= 5
    hbd = Descriptors.NumHDonors(mol) <= 5
    hba = Descriptors.NumHAcceptors(mol) <= 10
    return mw and logp and hbd and hba

Batch Descriptor Calculation

Calculate all descriptors at once:

from rdkit import Chem
from rdkit.Chem import Descriptors

mol = Chem.MolFromSmiles('CCO')

# Get all descriptors as dictionary
all_descriptors = Descriptors.CalcMolDescriptors(mol)

# Access specific descriptor
mw = all_descriptors['MolWt']
logp = all_descriptors['MolLogP']

# Get list of available descriptor names
from rdkit.Chem import Descriptors
descriptor_names = [desc[0] for desc in Descriptors._descList]

Descriptor Categories Summary

1. Physicochemical: MolWt, MolLogP, MolMR, TPSA
2. Topological: BertzCT, BalabanJ, Kappa indices
3. Electronic: Partial charges, E-state indices
4. Shape: Kappa indices, BCUT descriptors
5. Connectivity: Chi indices
6. 2D Fingerprints: FpDensity descriptors
7. Atom counts: Heavy atoms, heteroatoms, rings
8. Drug-likeness: QED, Lipinski parameters
9. Flexibility: NumRotatableBonds, HallKierAlpha
10. Surface area: VSA-based descriptors

Common Use Cases

Drug-likeness Screening

def screen_druglikeness(mol):
    return {
        'MW': Descriptors.MolWt(mol),
        'LogP': Descriptors.MolLogP(mol),
        'HBD': Descriptors.NumHDonors(mol),
        'HBA': Descriptors.NumHAcceptors(mol),
        'TPSA': Descriptors.TPSA(mol),
        'RotBonds': Descriptors.NumRotatableBonds(mol),
        'AromaticRings': Descriptors.NumAromaticRings(mol),
        'QED': Descriptors.qed(mol)
    }

Lead-like Filtering

def is_leadlike(mol):
    mw = 250 <= Descriptors.MolWt(mol) <= 350
    logp = Descriptors.MolLogP(mol) <= 3.5
    rot_bonds = Descriptors.NumRotatableBonds(mol) <= 7
    return mw and logp and rot_bonds

Diversity Analysis

def molecular_complexity(mol):
    return {
        'BertzCT': Descriptors.BertzCT(mol),
        'NumRings': Descriptors.RingCount(mol),
        'NumRotBonds': Descriptors.NumRotatableBonds(mol),
        'FractionCsp3': Descriptors.FractionCsp3(mol),
        'NumAromaticRings': Descriptors.NumAromaticRings(mol)
    }

Tips

1. Use batch calculation for multiple descriptors to avoid redundant computations
2. Check for None - some descriptors may return None for invalid molecules
3. Normalize descriptors for machine learning applications
4. Select relevant descriptors - not all 200+ descriptors are useful for every task
5. Consider 3D descriptors separately (require 3D coordinates)
6. Validate ranges - check if descriptor values are in expected ranges

references/workflows_and_best_practices.md (verbatim)

RDKit Workflows and Best Practices

Worked workflows (drug-likeness analysis, similarity screening, substructure filtering) followed by error handling, performance optimization, version-sensitive behavior, thread safety, and memory management.

Common Workflows

Drug-likeness Analysis

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

from rdkit import Chem
from rdkit.Chem import rdFingerprintGenerator
from rdkit import DataStructs

def similarity_screen(query_smiles, database_smiles, threshold=0.7):
    query_mol = Chem.MolFromSmiles(query_smiles)
    if query_mol is None:
        return []

    morgan_gen = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)
    query_fp = morgan_gen.GetFingerprint(query_mol)

    hits = []
    for idx, smiles in enumerate(database_smiles):
        mol = Chem.MolFromSmiles(smiles)
        if mol:
            fp = morgan_gen.GetFingerprint(mol)
            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

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:

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

Performance Optimization

Use safe storage formats:

import base64
import json
from pathlib import Path
from rdkit import Chem

# Portable exchange formats such as SMILES and SDF are safest for shared data.
# For local caches, RDKit's binary molecule representation avoids generic pickle.
payload = [base64.b64encode(mol.ToBinary()).decode("ascii") for mol in mols]
Path("molecules.rdmol.json").write_text(json.dumps(payload))

cached = json.loads(Path("molecules.rdmol.json").read_text())
mols = [Chem.Mol(base64.b64decode(item)) for item in cached]

Do not load Python pickle files from untrusted sources. Pickle deserialization can execute arbitrary code; prefer SMILES/SDF for interchange and RDKit binary payloads for trusted local caches.

Use bulk operations:

from rdkit import DataStructs
from rdkit.Chem import rdFingerprintGenerator

# Calculate fingerprints for all molecules at once
morgan_gen = rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048)
fps = [morgan_gen.GetFingerprint(mol) for mol in mols]

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

Version-Sensitive Behavior

Pin RDKit versions when exact molecular identifiers or numeric features are part of a persisted dataset, model feature pipeline, or regulated report. Recent releases changed or documented behavior in several Python-facing areas:

• Canonical SMILES and stereo: 2026.03 changed canonical double-bond handling to avoid stereo corruption, so some stereo-containing SMILES may differ from older releases.
• Descriptors and hashes: 2024.09 corrected unbranched-alkane fragment descriptor SMARTS and changed some tautomer/protomer hash outputs.
• Drawing: legacy rdkit.Chem.Draw canvas modules and functions such as MolToImageFile, MolToMPL, and MolToQPixmap were removed; use Draw.MolToFile, Draw.MolToImage, or rdMolDraw2D.
• MolStandardize: use rdkit.Chem.MolStandardize.rdMolStandardize; the older Python MolStandardize implementation was removed.
• Similarity maps: GetSimilarityMapFromWeights(), GetSimilarityMapForFingerprint(), and GetSimilarityMapForModel() now require an rdMolDraw2D drawing object.

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:

# 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)

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