---
title: "Rowan: Cloud-Native Molecular-Modeling and Drug-Design Workflows"
description: "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."
type: skill
canonical_url: https://claudary.paisolsolutions.com/skills/skill-323
source: "Claudary"
difficulty: intermediate
author: "Claude Code Knowledge Pack"
date: 2026-07-10T11:44:32.822Z
license: CC-BY-4.0
attribution: "Rowan: Cloud-Native Molecular-Modeling and Drug-Design Workflows — Claudary (https://claudary.paisolsolutions.com/skills/skill-323)"
---

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

## Overview

---
name: rowan
description: Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.
license: Proprietary (API key required)
compatibility: Python 3.12+, API key required
metadata:
  skill-author: Rowan Science
  trigger-keywords: ["pKa prediction", "molecular docking", "conformer search", "chemistry workflow", "drug discovery", "SMILES", "protein structure", "batch molecular modeling", "cloud chemistry"]
---

# 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

## Access and pricing model

Rowan uses a credit-based usage model. All users, including free-tier users, can create API keys and use the Python API.

### Free-tier access

- Access to all Rowan core workflows
- 20 credits per week
- 500 signup credits

### Pricing and credit consumption

Credits are consumed according to compute type:

- **CPU**: 1 credit per minute
- **GPU**: 3 credits per minute
- **H100/H200 GPU**: 7 credits per minute

Purchased credits are priced per credit and remain valid for up to one year from purchase.

### Typical cost estimates

| Workflow | Typical Runtime | Estimated Credits | Notes |
|----------|----------------|-------------------|-------|
| Descriptors | <1 min | 0.5–2 | Lightweight, good for triage |
| pKa (single transition) | 2–5 min | 2–5 | Depends on molecule size |
| MacropKa (pH 0–14) | 5–15 min | 5–15 | Broader sampling, higher cost |
| Conformer search | 3–10 min | 3–10 | Ensemble quality matters |
| Tautomer search | 2–5 min | 2–5 | Heterocyclic systems |
| Docking (single ligand) | 5–20 min | 5–20 | Depends on pocket size, refinement |
| Analogue docking series (10–50 ligands) | 30–120 min | 30–100+ | Shared reference frame |
| MSA generation | 5–30 min | 5–30 | Sequence length dependent |
| Protein-ligand cofolding | 15–60 min | 20–50+ | AI structure prediction, GPU-heavy |

## 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: 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}")
```

### Webhook secret management

For webhook signature verification, manage secrets through your user account:

```python
import rowan

# Get your current webhook secret (returns None if none exists)
secret = rowan.get_webhook_secret()
if secret is None:
    secret = rowan.create_webhook_secret()
print(f"Secret key: {secret.secret}")

# Rotate your secret (invalidates old, creates new)
# Use this periodically for security
new_secret = rowan.rotate_webhook_secret()
print(f"New secret created (old secret disabled): {new_secret.secret}")

# Verify incoming webhook signatures
is_valid = rowan.verify_webhook_secret(
    request_body=b"...",           # Raw request body (bytes)
    signature="X-Rowan-Signature", # From request header
    secret=secret.secret
)
```

## 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 |

## Common workflow categories

### 1. Descriptors

A lightweight entry point for batch triage, SAR, or exploratory scripts.

```python
wf = rowan.submit_descriptors_workflow(
    "CC(=O)Oc1ccccc1C(=O)O",  # positional arg, accepts SMILES string
    name="aspirin descriptors",
)

result = wf.result()
print(result.descriptors['MW'])    # 180.16
print(result.descriptors['SLogP']) # 1.19
print(result.descriptors['TPSA'])  # 59.44
print(result.data['descriptors'])
# {'MW': 180.16, 'SLogP': 1.19, 'TPSA': 59.44, 'nHBDon': 1.0, 'nHBAcc': 4.0, ...}
```

**Common descriptor keys:**

| Key | Description | Typical drug range |
|-----|-------------|-------------------|
| `MW` | Molecular weight (Da) | <500 (Lipinski) |
| `SLogP` | Calculated LogP (lipophilicity) | -2 to +5 |
| `TPSA` | Topological polar surface area (Ų) | <140 for oral bioavailability |
| `nHBDon` | H-bond donor count | ≤5 (Lipinski) |
| `nHBAcc` | H-bond acceptor count | ≤10 (Lipinski) |
| `nRot` | Rotatable bond count | <10 for oral drugs |
| `nRing` | Ring count | — |
| `nHeavyAtom` | Heavy atom count | — |
| `FilterItLogS` | Estimated aqueous solubility (LogS) | >-4 preferred |
| `Lipinski` | Lipinski Ro5 pass (1.0) or fail (0.0) | — |

The result contains hundreds of additional molecular descriptors (BCUT, GETAWAY, WHIM, etc.); access any via `result.descriptors['key']`.

### 2. Microscopic pKa

For protonation-state energetics and acid/base behavior of a specific structure.

Two methods are available:

| Method | Input | Speed | Covers | Use when |
|--------|-------|-------|--------|----------|
| `chemprop_nevolianis2025` | SMILES string | Fast | Deprotonation only (anionic conjugate bases) | Acidic groups only; quick screening |
| `starling` | SMILES string | Fast | Acid + base (full protonation/deprotonation) | Most drug-like molecules; preferred SMILES method |
| `aimnet2_wagen2024` (default) | 3D molecule object | Slower, higher accuracy | Acid + base | You already have a 3D structure (e.g. from conformer search) |

```python
# Fast path: SMILES input with full acid+base coverage (use starling method when available)
wf = rowan.submit_pka_workflow(
    initial_molecule="c1ccccc1O",       # phenol SMILES; param is initial_molecule, not initial_smiles
    method="starling",   # fast SMILES method, covers acid+base; chemprop_nevolianis2025 is deprotonation-only
    name="phenol pKa",
)

result = wf.result()
print(result.strongest_acid)    # 9.81 (pKa of the most acidic site)
print(result.conjugate_bases)   # list of {pka, smiles, atom_index, ...} per deprotonatable site
```

### 3. MacropKa

For pH-dependent protonation behavior across a range.

```python
wf = rowan.submit_macropka_workflow(
    initial_smiles="CN1CCN(CC1)C2=NC=NC3=CC=CC=C32",  # imidazole
    min_pH=0,
    max_pH=14,
    min_charge=-2,  # default
    max_charge=2,   # default
    compute_aqueous_solubility=True,  # default
    name="imidazole macropKa",
)

result = wf.result()
print(result.pka_values)               # list of pKa values
print(result.logd_by_ph)               # dict of {pH: logD}
print(result.aqueous_solubility_by_ph) # dict of {pH: solubility}
print(result.isoelectric_point)        # isoelectric point
print(result.data)
# {'pKa_values': [...], 'logD_by_pH': {...}, 'aqueous_solubility_by_pH': {...}, ...}
```

### 4. Conformer search

For 3D ensemble generation when ensemble quality matters.

```python
wf = rowan.submit_conformer_search_workflow(
    initi

---

Source: [Claudary](https://claudary.paisolsolutions.com/skills/skill-323) · https://claudary.paisolsolutions.com