# Rowan

> Rowan es una plataforma cloud de modelado molecular y química medicinal con API en Python: predicción de pKa/macropKa, conformeros, tautómeros, docking, cofolding proteína-ligando, MSA, dinámica molecular y permeabilidad.

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

Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills
Autor: K-Dense-AI
Licencia: Proprietary (API key required)
Actualizado: hace 28 días
Coste de contexto: 128 tok instalada, 3.3k tok al activarse, 9.9k tok con todos los archivos del bundle
Bundle: 6 archivos, 39 KB
Permisos que pide: ninguno declarado

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

## Qué hace

- Ejecuta workflows en la nube de química medicinal: pKa/macropKa, conformeros, tautómeros, docking y cofolding proteína-ligando
- Expone un patrón submit/wait/retrieve con objetos de resultado tipados por workflow
- Organiza campañas con proyectos y carpetas, y soporta envío por lotes y webhooks
- Evita mantener infraestructura HPC/GPU local para química computacional
- Encadena workflows multi-paso, por ejemplo tautómero → docking → refinamiento de pose

## Cuándo usarla

- Se necesita predicción de pKa, descriptores, permeabilidad o solubilidad por lotes
- Se ejecuta docking, docking de análogos, cofolding proteína-ligando o generación de MSA
- Se necesita una campaña de química medicinal programática sin mantener HPC/GPU propios
- Se encadenan pasos de modelado molecular en un pipeline (tautómero, pKa, docking, MD)

## Cuándo no

- Es E/S molecular simple (usar RDKit directamente)
- Se necesitan cálculos ab initio post-HF o relativistas

## Qué la activa

- "Predice el pKa de esta molécula con Rowan"
- "Genera conformeros y tautómeros de este compuesto y luego hazle docking"
- "Corre un docking de análogos para esta serie de compuestos frente a esta proteína"
- "Predice el complejo proteína-ligando de esta secuencia sin estructura cristalina"

## Antes de instalar

- Requiere Python 3.12+ y una API key de Rowan (ROWAN_API_KEY); el acceso es de pago (licencia propietaria con clave API).

## Archivos

- SKILL.md — 13 KB
- references/access_and_pricing.md — 1 KB
- references/batch_and_webhooks.md — 7 KB
- references/end_to_end_example.md — 4 KB
- references/troubleshooting.md — 2 KB
- references/workflow_catalog.md — 11 KB

## SKILL.md

Reproducido tal cual desde K-Dense-AI/scientific-agent-skills bajo Proprietary (API key required). Esta sección es el documento original y está en inglés.

# Rowan: Cloud-Native Molecular-Modeling and Drug-Design Workflows

## Overview

Rowan is a cloud-native workflow platform for molecular simulation, medicinal chemistry, and structure-based design. Its Python API exposes a unified interface for small-molecule modeling, property prediction, docking, molecular dynamics, and AI structure workflows.

Use Rowan when you want to run medicinal-chemistry or molecular-design workflows programmatically without maintaining local HPC infrastructure, GPU provisioning, or a collection of separate modeling tools. Rowan handles all infrastructure, result management, and computation scaling.

## When to use Rowan

**Rowan is a good fit for:**

- Quantum chemistry, semiempirical methods, or neural network potentials
- Batch property prediction (pKa, descriptors, permeability, solubility)
- Conformer and tautomer ensemble generation
- Docking workflows (single-ligand, analogue series, pose refinement)
- Protein-ligand cofolding and MSA generation
- Multi-step chemistry pipelines (e.g., tautomer search → docking → pose analysis)
- Batch medicinal-chemistry campaigns where you need consistent, scalable infrastructure

**Rowan is not the right fit for:**
- Simple molecular I/O (use RDKit directly)
- Post-HF *ab initio* quantum chemistry or relativistic calculations

## Quick start

```bash
uv pip install rowan-python
```

```python
import rowan
rowan.api_key = "your_api_key_here"  # or set ROWAN_API_KEY env var

# Submit a descriptors workflow — completes in under a minute
wf = rowan.submit_descriptors_workflow("CC(=O)Oc1ccccc1C(=O)O", name="aspirin")
result = wf.result()

print(result.descriptors['MW'])    # 180.16
print(result.descriptors['SLogP']) # 1.19
print(result.descriptors['TPSA'])  # 59.44
```

If that prints without error, you're set up correctly.

## Installation

```bash
uv pip install rowan-python
# or: uv pip install rowan-python
```

## User and webhook management

### Authentication

Set an API key via environment variable (recommended):

```bash
export ROWAN_API_KEY="your_api_key_here"
```

Or set directly in Python:

```python
import rowan
rowan.api_key = "your_api_key_here"
```

Verify authentication:

```python
import rowan
user = rowan.whoami()  # Returns user info if authenticated
print(f"User: {user.email}")
print(f"Credits available: {user.credits_available_string}")
```

## Molecule input formats

Rowan accepts molecules in the following formats:

- **SMILES** (preferred): `"CCO"`, `"c1ccccc1O"`
- **SMARTS patterns** (for some workflows): subset of SMARTS for substructure matching
- **InChI** (if supported in your API version): `"InChI=1S/C2H6O/c1-2-3/h3H,2H2,1H3"`

The API will validate input and raise a `rowan.ValidationError` if a molecule cannot be parsed. Always use canonicalized SMILES for reproducibility.

**Tip:** Use RDKit to validate SMILES before submission:

```python
from rdkit import Chem
smiles = "CCO"
mol = Chem.MolFromSmiles(smiles)
if mol is None:
    raise ValueError(f"Invalid SMILES: {smiles}")
```

## Core usage pattern

Most Rowan tasks follow the same three-step pattern:

1. **Submit** a workflow
2. **Wait** for completion (with optional streaming)
3. **Retrieve** typed results with convenience properties

```python
import rowan

# 1. Submit — use the specific workflow function (not the generic submit_workflow)
workflow = rowan.submit_descriptors_workflow(
    "CC(=O)Oc1ccccc1C(=O)O",
    name="aspirin descriptors",
)

# 2. & 3. Wait and retrieve
result = workflow.result()  # Blocks until done (default: wait=True, poll_interval=5)
print(result.data)              # Raw dict
print(result.descriptors['MW']) # 180.16 — use result.descriptors dict, not result.molecular_weight
```

For long-running workflows, use streaming:

```python
for partial in workflow.stream_result(poll_interval=5):
    print(f"Progress: {partial.complete}%")
    print(partial.data)
```

### result() vs. stream_result()

| Pattern | Use When | Duration |
|---------|----------|----------|
| `result()` | You can wait for the full result | <5 min typical |
| `stream_result()` | You want progress feedback or need early partial results | >5 min, or interactive use |

**Guideline:** Use `result()` for descriptors, pKa. Use `stream_result()` for conformer search, docking, cofolding.

## Working with results

Rowan's API includes **typed workflow result objects** with convenience properties.

### Using typed properties and .data

Results have two access patterns:

1. **Convenience properties** (recommended first): `result.descriptors`, `result.best_pose`, `result.conformer_energies`
2. **Raw fallback**: `result.data` — raw dictionary from the API

Example:

```python
result = rowan.submit_descriptors_workflow(
    "CCO",
    name="ethanol",
).result()

# Convenience property (returns dict of all descriptors):
print(result.descriptors['MW'])   # 46.042
print(result.descriptors['SLogP'])  # -0.001
print(result.descriptors['TPSA'])   # 57.96

# Raw data fallback (descriptors are nested under 'descriptors' key):
print(result.data['descriptors'])
# {'MW': 46.042, 'SLogP': -0.001, 'TPSA': 57.96, 'nHBDon': 1.0, 'nHBAcc': 1.0, ...}
```

**Note:** `DescriptorsResult` does **not** have a `molecular_weight` property. Descriptor keys use short names (`MW`, `SLogP`, `nHBDon`) not verbose names.

### Cache invalidation

Some result properties are lazily loaded (e.g., conformer geometries, protein structures). To refresh:

```python
result.clear_cache()
new_structures = result.conformer_molecules  # Refetched
```

## Projects, folders, and organization

For nontrivial campaigns, use projects and folders to keep work organized.

### Projects

```python
import rowan

# Create a project
project = rowan.create_project(name="CDK2 lead optimization")
rowan.set_project("CDK2 lead optimization")

# All subsequent workflows go into this project
wf = rowan.submit_descriptors_workflow("CCO", name="test compound")

# Retrieve later
project = rowan.retrieve_project("CDK2 lead optimization")
workflows = rowan.list_workflows(project=project, size=50)
```

### Folders

```python
# Create a hierarchical folder structure
folder = rowan.create_folder(name="docking/batch_1/screening")

wf = rowan.submit_docking_workflow(
    # ... docking params ...
    folder=folder,
    name="compound_001",
)

# List workflows in a folder
results = rowan.list_workflows(folder=folder)
```

## Workflow decision trees

### pKa vs. MacropKa

**Use microscopic pKa when:**

- You need the pKa of a single ionizable group
- You're interested in acid–base transitions and protonation thermodynamics
- The molecule has one or two ionizable sites
- Speed is critical (faster, fewer credits)

**Use macropKa when:**

- You need pH-dependent behavior across a physiologically relevant range (e.g., 0–14)
- You want aggregated charge and protonation-state populations across pH
- The molecule has multiple ionizable groups with coupled protonation
- You need downstream properties like aqueous solubility at different pH

**Example decision:**

```text
Phenol (pKa ~10): Use microscopic pKa
Amine (pKa ~9–10): Use microscopic pKa
Multi-ionizable drug (N, O, acidic group): Use macropKa
ADME assessment across GI pH: Use macropKa
```

### Conformer search vs. tautomer search

**Use conformer search when:**

- A single tautomeric form is known
- You need a diverse 3D ensemble for docking, MD, or SAR analysis
- Rotatable bonds dominate the chemical space

**Use tautomer search when:**

- Tautomeric equilibrium is uncertain (e.g., heterocycles, keto–enol systems)
- You need to model all relevant protonation isomers
- Downstream calculations (docking, pKa) depend on tautomeric form

**Combined workflow:**

```python
# Step 1: Find best tautomer
taut_wf = rowan.submit_tautomer_search_workflow(
    initial_molecule="O=c1[nH]ccnc1",
    name="imidazole tautomers",
)
best_taut = taut_wf.result().best_tautomer

# Step 2: Generate conformers from best tautomer
conf_wf = rowan.submit_conformer_search_workflow(
    initial_molecule=best_taut,
    name="imidazole conformers",
)
```

### Docking vs. analogue docking vs. cofolding

| Workflow | Use When | Input | Output |
|----------|----------|-------|--------|
| Docking | Single ligand, known pocket | Protein + SMILES + pocket coords | Pose, score, dG |
| Analogue docking | 5–100+ related compounds | Protein + SMILES list + reference ligand | All poses, reference-aligned |
| Protein-ligand cofolding | Sequence + ligand, no crystal structure | Protein sequence + SMILES | ML-predicted bound complex |

## Protein utilities

### Upload proteins

```python
# From local PDB file
protein = rowan.upload_protein(
    name="egfr_kinase_domain",
    file_path="egfr_kinase.pdb",
)

# From PDB database
protein_from_pdb = rowan.create_protein_from_pdb_id(
    name="CDK2 (1M17)",
    code="1M17",
)

# Retrieve previously uploaded protein
protein = rowan.retrieve_protein("protein-uuid")

# List all proteins
my_proteins = rowan.list_proteins()
```

### Protein preparation guidance

- **File format**: PDB, mmCIF (Rowan auto-detects)
- **Water molecules**: Rowan usually keeps relevant water; remove bulk water beforehand if desired
- **Heteroatoms**: Cofactors, ions, and bound ligands are usually preserved; remove unwanted heteroatoms before upload
- **Multi-chain proteins**: Fully supported
- **Resolution**: Works with NMR structures, homology models, and cryo-EM; quality matters for downstream predictions
- **Validation**: Rowan validates PDB syntax; severely malformed files may be rejected

## Workflow catalog

Nine common workflow categories — descriptors, microscopic pKa, MacropKa, conformer
search, tautomer search, docking, analogue docking, MSA generation, and protein-ligand
cofolding — each with submission code and result shapes, plus the complete list of every
supported workflow type (core modeling, structure-based design, advanced computational
chemistry, reaction chemistry, advanced properties, binding free energy, and sequence and
structural biology) are in
[references/workflow_catalog.md](references/workflow_catalog.md).

## Batch submission, webhooks, and asynchronous work

Batch submit/poll/retrieve, the non-blocking fire-and-check pattern, webhook setup,
secret creation and rotation, payload and signature verification (with a FastAPI
handler), and webhook best practices are in
[references/batch_and_webhooks.md](references/batch_and_webhooks.md).

## Access, pricing, and credits

Free-tier limits, credit consumption per workflow, and typical cost estimates are in
[references/access_and_pricing.md](references/access_and_pricing.md).

## Worked example and troubleshooting

A full lead-optimization campaign — project setup, tautomers, pKa across an analogue
series, result collection, and a docking follow-up — is in
[references/end_to_end_example.md](references/end_to_end_example.md).

Common errors with their fixes, and debugging tips, are in
[references/troubleshooting.md](references/troubleshooting.md).

## Recommended usage patterns

- **Prefer Rowan-native workflows** over low-level assembly when they exist
- **Use projects and folders** for any nontrivial campaign (>5 workflows)
- **Use `result()` to block until complete** (default: `wait=True, poll_interval=5`)
- **Use typed result properties first**, fall back to `.data` for unmapped fields
- **Use batch submission** for compound libraries or analogue series
- **Chain workflows** for multi-step chemistry campaigns:
  - `pKa → macropKa → permeability` (ADME assessment)
  - `tautomer search → docking → pose-analysis MD` (pose refinement)
  - `MSA generation → protein-ligand cofolding` (AI structure prediction)
- **Use webhooks** for long-running campaigns (>50 workflows) or asynchronous pipelines
- **Use streaming** for interactive feedback on large conformer/docking searches

## Summary

Use Rowan when your workflow requires cloud execution for molecular-design tasks, especially when you want one unified API and consistent result handling across small-molecule modeling, proteins, docking, ADME prediction, and ML structure generation.

Rowan is a molecular-design workflow platform, not just a remote chemistry engine. It handles infrastructure scaling, result persistence, and multi-step pipeline orchestration so you can focus on science.

## 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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