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K-Dense-AI/scientific-agent-skills/skills/rdkit/SKILL.md

rdkit

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.

Source repository stars
31,966
Declared platforms
0
Static risk flags
0
Last source update
2026-07-28
Source checked
2026-07-28
DecisionCompatibilityInstallWorkflowPermissionsEvidenceSource

Decision brief

What it does—and where it fits

Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions.

Best for

    Not for

    • Forgetting to check for None: Always validate molecules after parsing
    • Sanitization failures: Use DetectChemistryProblems() to debug

    Compatibility matrix

    Platform support, with evidence labels

    PlatformStatusEvidenceWhat to check
    CodexNot declaredNo explicit evidencePortability before use
    Claude CodeNot declaredNo explicit evidencePortability before use
    CursorNot declaredNo explicit evidencePortability before use
    Gemini CLINot declaredNo explicit evidencePortability before use
    Open the compatibility checker

    Installation

    Inspect first. Install second.

    The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

    Source-detected install commandSource
    npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill "skills/rdkit"
    Safe inspection promptEditorial

    Inspect the Agent Skill "rdkit" from https://github.com/K-Dense-AI/scientific-agent-skills/blob/e7ac42510774624f327003c95b6650e2883bc01d/skills/rdkit/SKILL.md at commit e7ac42510774624f327003c95b6650e2883bc01d. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

    Workflow

    What the source asks the agent to do

    1. 01

      Installation and Setup

      Use uv when installing into an existing Python environment:

      Use uv when installing into an existing Python environment:For reproducible chemistry environments, especially when mixing compiled scientific packages, conda-forge remains the upstream recommendation: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.
    2. 02

      Core Capabilities

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

      Twelve capability areas, each with worked code, are documented in references/corecapabilities.md:Worked workflows and the performance, thread-safety, and version-sensitivity notes are in references/workflowsandbestpractices.md.Prefer portable exchange formats (SMILES, SDF) for shared data; for local caches RDKit's binary molecule representation avoids generic pickle.
    3. 03

      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 vi…

      Forgetting to check for None: Always validate molecules after parsingSanitization failures: Use DetectChemistryProblems() to debugMissing hydrogens: Use AddHs() when calculating properties that depend on hydrogen
    4. 04

      references/

      This skill includes detailed API reference documentation:

      apireference.md - Comprehensive listing of RDKit modules, functions, and classes organized by functionalitydescriptorsreference.md - Complete list of available molecular descriptors with descriptionssmartspatterns.md - Common SMARTS patterns for functional groups and structural features
    5. 05

      scripts/

      Example scripts for common RDKit workflows:

      molecularproperties.py - Calculate comprehensive molecular properties and descriptorssimilaritysearch.py - Perform fingerprint-based similarity screeningsubstructurefilter.py - Filter molecules by substructure patterns

    Permission review

    Static risk signals and limitations

    No configured static risk pattern was detected

    This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

    Evidence record

    Why each signal appears

    EvidenceSourceComputedTestedEditorial
    SignalValueEvidence typeMeaning
    Quality score79/100ComputedDocumentation, specificity, maintenance, and trust rules
    Repository stars31,966SourceRepository attention, not individual Skill quality
    Compatibility0 platformsSourceDeclared in the catalog source record
    Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

    Pinned source

    Provenance and original SKILL.md

    Repository
    K-Dense-AI/scientific-agent-skills
    Skill path
    skills/rdkit/SKILL.md
    Commit
    e7ac42510774624f327003c95b6650e2883bc01d
    License
    MIT
    Collected
    2026-07-28
    Default branch
    main
    View the original SKILL.md

    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:

    #AreaCovers
    1Molecular I/O and creationSMILES, MOL files and blocks, InChI, SDF and SMILES suppliers, multithreaded reading, writers
    2Sanitization and validationdisabling automatic sanitization, manual and partial sanitization, detecting problems first
    3Analysis and propertiesatom and bond iteration, ring information and SSSR, chirality and stereochemistry, fragments
    4DescriptorsMW, LogP, TPSA, H-bond donors/acceptors, rotatable bonds, aromatic rings, bulk calculation, drug-likeness
    5Fingerprints and similaritytopological, Morgan/ECFP via rdFingerprintGenerator, MACCS, atom pair, torsion, Avalon; Tanimoto and other metrics; Butina clustering
    6Substructure searchingSMARTS queries, match retrieval, and a library of common patterns
    7Chemical reactionsreaction SMARTS, applying reactions, reaction fingerprints
    82D and 3D coordinatesdepiction, template alignment, ETKDG embedding, force-field optimization, RMSD, constrained embedding
    9Visualizationsingle and grid images, substructure highlighting, custom drawer options, Jupyter integration, fingerprint bit environments
    10Molecular modificationexplicit hydrogens, Kekulization, aromaticity, substructure replacement, charge neutralization
    11Hashes and standardizationMurcko scaffold and canonical hashes, regioisomer hashes, randomized SMILES for augmentation
    12Pharmacophore and 3D featuresfeature 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.