# Rdkit

> Kit de cheminformática para control molecular fino: parsing SMILES/SDF, descriptores, fingerprints, búsqueda de subestructuras, generación 2D/3D, similitud y reacciones. Para flujos estándar más simples, usa datamol.

Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/rdkit
Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/rdkit.md

Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills
Autor: K-Dense-AI
Licencia: BSD-3-Clause license
Actualizado: el mes pasado
Coste de contexto: 86 tok instalada, 1.4k tok al activarse, 23.3k tok con todos los archivos del bundle
Bundle: 9 archivos, 91 KB
Permisos que pide: read write edit bash

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add K-Dense-AI/scientific-agent-skills --skill rdkit --agent claude-code
# Cursor
npx -y skills add K-Dense-AI/scientific-agent-skills --skill rdkit --agent cursor
# Codex
npx -y skills add K-Dense-AI/scientific-agent-skills --skill rdkit --agent codex
# Gemini CLI
npx -y skills add K-Dense-AI/scientific-agent-skills --skill rdkit --agent gemini
# Windsurf
npx -y skills add K-Dense-AI/scientific-agent-skills --skill rdkit --agent windsurf
# Cline
npx -y skills add K-Dense-AI/scientific-agent-skills --skill rdkit --agent cline
```

## Qué hace

- Parsea y escribe estructuras moleculares en SMILES, MOL, InChI y SDF
- Calcula descriptores moleculares (MW, LogP, TPSA, donantes/aceptores de H)
- Genera fingerprints (Morgan/ECFP, MACCS, topológicos) y calcula similitud con clustering Butina
- Ejecuta búsquedas de subestructuras con SMARTS y aplica reacciones químicas
- Genera coordenadas 2D/3D y optimiza geometrías con campos de fuerza

## Cuándo usarla

- Se necesita control fino sobre sanitización, algoritmos o parsing molecular que datamol no ofrece
- Se trabaja en descubrimiento de fármacos, química computacional o cheminformática
- Se calculan descriptores, fingerprints o similitud molecular por lotes
- Se necesita búsqueda de subestructuras o aplicar reacciones SMARTS

## Cuándo no

- Se busca una interfaz más simple para flujos estándar (usar datamol en su lugar)

## Qué la activa

- "Calcula el peso molecular y el LogP de esta lista de SMILES"
- "Busca subestructuras con este patrón SMARTS en mi conjunto de moléculas"
- "Genera fingerprints Morgan y agrupa estas moléculas por similitud"
- "Genera conformeros 3D de esta molécula y optimízalos"

## Antes de instalar

- Requiere instalar RDKit vía `uv pip install rdkit` o conda-forge (paquete `rdkit`); `rdkit-pypi` es el nombre legado en PyPI.

## Archivos

- SKILL.md — 6 KB
- references/api_reference.md — 17 KB
- references/core_capabilities.md — 15 KB
- references/descriptors_reference.md — 12 KB
- references/smarts_patterns.md — 8 KB
- references/workflows_and_best_practices.md — 5 KB
- scripts/molecular_properties.py — 7 KB
- scripts/similarity_search.py — 9 KB
- scripts/substructure_filter.py — 12 KB

## SKILL.md

Reproducido tal cual desde K-Dense-AI/scientific-agent-skills bajo BSD-3-Clause license. Esta sección es el documento original y está en inglés.

# 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:

```bash
uv pip install rdkit
```

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

```bash
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](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](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.

## Dónde encaja

- Categoría: [Datos y analítica](https://skillsagentes.com/categorias/datos-analitica.md) — Consulta, limpia y visualiza datos sin salir del agente.
- Creador: [K-Dense-AI](https://skillsagentes.com/creators/k-dense-ai.md) — 163 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

## Otras skills del mismo repositorio

- [Citation Management](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/citation-management.md): Gestión integral de citas académicas: busca en OpenAlex, PubMed y Google Scholar, extrae metadatos precisos, valida citas y genera entradas BibTeX correctamente formateadas.
- [Scientific Slides](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scientific-slides.md): Crea decks de diapositivas y presentaciones para charlas de investigación: PowerPoint, presentaciones de conferencia, seminarios, defensas de tesis. Da estructura, plantillas, guía de tiempos y validación visual.
- [Literature Review](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/literature-review.md): Realiza revisiones bibliográficas sistemáticas y completas usando varias bases académicas (PubMed, arXiv, bioRxiv, Semantic Scholar). Genera markdown y PDF con citas verificadas en varios estilos (APA, Nature, Vancouver).
- [Infographics](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/infographics.md): Crea infografías profesionales con Nano Banana Pro AI y refinamiento iterativo inteligente. Usa Gemini 3.6 Flash para revisar la calidad e integra investigación con Perplexity Sonar. Soporta 10 tipos, 8 estilos y paletas para daltonismo.
- [Latex Posters](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/latex-posters.md): Crea pósteres de investigación profesionales en LaTeX con beamerposter, tikzposter o baposter, para conferencias y comunicación científica: layout, colores, columnas múltiples e integración de figuras.

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