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Agent skill

RDKit Cheminformatics

by davila7 in davila7/claude-code-templates

Guides molecular work with RDKit in Python: reading SMILES and SDF, sanitization, descriptors, fingerprints, substructure and similarity search, reactions and coordinates.

MITAuto-check passedResearch & Science

Install RDKit Cheminformatics

skills CLI
$ npx skills add davila7/claude-code-templates --skill rdkit -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install davila7/claude-code-templates rdkit --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-tool/components/skills/scientific/rdkit .claude/skills/rdkit && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
rdkit
GitHub stars
32k
Used in
15 other repos
Token cost
~5k tokens
SKILL.md length
569 words
Files
7 (incl. scripts, references)
Skills in repo
477
Repo updated
First seen
Licence
MIT

At a glance

Guides molecular work with RDKit in Python: reading SMILES and SDF, sanitization, descriptors, fingerprints, substructure and similarity search, reactions and coordinates.

  • Works in 12 steps: Molecular I/O and Creation → Molecular Sanitization and Validation → Molecular Analysis and Properties → …
  • Parsing SMILES or SDF files and validating the molecules
  • SKILL.md covers Overview, Core Capabilities, Common Workflows and Best Practices, plus 2 more sections
  • Runs Python scripts from its folder

What it does

The skill covers RDKit's Python API for drug discovery and computational chemistry. It shows how to read and write molecules from SMILES, SDF and other formats, batch-process files with supplier and writer objects, and handle failures: parsing functions return None on error, so the agent has to check for it before using a molecule.

Sanitization gets detailed treatment. RDKit runs 13 steps on import, covering valence checks, aromaticity perception and chirality assignment, and the skill shows how to turn it off and what errors to expect. Further sections cover atom and bond inspection, stereochemistry, fragments, descriptors such as MW, LogP and TPSA, fingerprints, substructure and similarity search, reactions and 2D and 3D generation. Three scripts handle molecular properties, similarity search and substructure filtering, and for simpler standard workflows the skill points to datamol.

When your agent uses it

  • Parsing SMILES or SDF files and validating the molecules
  • Calculating molecular weight, LogP, TPSA and other descriptors
  • Filtering a compound library by substructure or SMARTS pattern
  • Ranking compounds by fingerprint similarity to a query molecule
  • Generating 2D or 3D coordinates for molecules

Example prompts

  • “Read compounds.sdf, drop molecules that fail sanitization and report molecular weight and LogP for the rest.”
  • “Find every molecule in library.smi that contains a carboxylic acid group.”
  • “Rank the molecules in actives.sdf by fingerprint similarity to aspirin.”
  • “Generate 3D conformers for this SMILES string and save them as an SDF file.”

Requirements

  • Python with `rdkit`

Workflow steps

12 steps, taken from the step headings in SKILL.md.

  1. Molecular I/O and Creation
  2. Molecular Sanitization and Validation
  3. Molecular Analysis and Properties
  4. Molecular Descriptors and Properties
  5. Fingerprints and Molecular Similarity
  6. Substructure Searching and SMARTS
  7. Chemical Reactions
  8. 2D and 3D Coordinate Generation
  9. Molecular Visualization
  10. Molecular Modification
  11. Working with Molecular Hashes and Standardization
  12. Pharmacophore and 3D Features

What it can do on your machine

Read from SKILL.md and the folder at commit 14680ec. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 3 files in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

RDKit Cheminformatics loads about 5k tokens when it runs, and up to ~14k if it reads all its reference files. Until then it costs about 88 tokens; SKILL.md has 569 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~88
When it runs · the whole SKILL.md, loaded when a task matches
~5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~14k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from davila7/claude-code-templates at commit 14680ec, republished under its MIT licence (© davila7). 569 words, ~4,975 tokens.

Download SKILL.mdSave it as .claude/skills/rdkit/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
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.

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)
Show full SKILL.md (225 more words)Show less
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.

© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (scripts, references) in cli-tool/components/skills/scientific/rdkit of davila7/claude-code-templates.

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

Open the folder on GitHubat commit 14680ec

Used in 15 other repositories

We found 34 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 15 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

Compare with similar skills

RDKit Cheminformatics next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

RDKit Cheminformatics compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
RDKit Cheminformatics this skilldavila7/claude-code-templates32k15 repos~5kAutomated safety check: PassMIT
DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills48k1 repos~3kAutomated safety check: NotesMIT
Edu Chem Reactionwy51ai/edulab1.4k—~1.2kAutomated safety check: PassApache-2.0
RDKit Conformer Generatorjinzhezenggroup/computational-chemistry-agent-skills1481 repos~2.4kAutomated safety check: PassLGPL-3.0
RDKit Descriptors and Fingerprintsjinzhezenggroup/computational-chemistry-agent-skills1481 repos~2.3kAutomated safety check: PassLGPL-3.0
Rowanlamm-mit/scienceclaw2444 repos~3.1kAutomated safety check: WarnProprietary

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Works with

Categories

Questions about RDKit Cheminformatics

What does RDKit Cheminformatics do?

Guides molecular work with RDKit in Python: reading SMILES and SDF, sanitization, descriptors, fingerprints, substructure and similarity search, reactions and coordinates. The skill covers RDKit's Python API for drug discovery and computational chemistry. It shows how to read and write molecules from SMILES, SDF and other formats, batch-process files with supplier and writer objects, and handle failures: parsing functions return None on error, so the agent has to check for it before using a molecule.

When should I use RDKit Cheminformatics?

RDKit Cheminformatics fits situations like: parsing SMILES or SDF files and validating the molecules; calculating molecular weight, LogP, TPSA and other descriptors; filtering a compound library by substructure or SMARTS pattern; ranking compounds by fingerprint similarity to a query molecule.

How do I install RDKit Cheminformatics in Claude Code?

Run `npx skills add davila7/claude-code-templates --skill rdkit -a claude-code`. Or copy the skill folder (cli-tool/components/skills/scientific/rdkit in davila7/claude-code-templates) into .claude/skills/rdkit in your project. Claude Code loads it when a task matches its description.

How do I install RDKit Cheminformatics in Codex?

Run `npx skills add davila7/claude-code-templates --skill rdkit -a codex`. Or copy the skill folder (cli-tool/components/skills/scientific/rdkit in davila7/claude-code-templates) into .agents/skills/rdkit in your project. Codex loads it when a task matches its description.

Can I use RDKit Cheminformatics in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add davila7/claude-code-templates --skill rdkit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/rdkit, .gemini/skills/rdkit, .github/skills/rdkit and .opencode/skills/rdkit in your project.

What does RDKit Cheminformatics need to run?

Going by SKILL.md and its folder, RDKit Cheminformatics needs Python for the scripts in its folder. Our summary lists: Python with `rdkit`.

Does RDKit Cheminformatics access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is RDKit Cheminformatics safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does RDKit Cheminformatics use?

RDKit Cheminformatics is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does RDKit Cheminformatics use?

About 5k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 9.2k tokens, read only when the agent opens those files.

What are the alternatives to RDKit Cheminformatics?

Skills that share tags, products or a category with RDKit Cheminformatics: DiffDock Molecular Docking (K-Dense-AI/scientific-agent-skills, 48k stars), Edu Chem Reaction (wy51ai/edulab, 1.4k stars), RDKit Conformer Generator (jinzhezenggroup/computational-chemistry-agent-skills, 148 stars) and RDKit Descriptors and Fingerprints (jinzhezenggroup/computational-chemistry-agent-skills, 148 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains RDKit Cheminformatics?

davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,463 GitHub stars. The repository holds 477 skills in this directory. The repository was last updated on October 8, 2026.

Source: davila7/claude-code-templates on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.