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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: version: "1.5" skill-author: Rowan Science trigger-keywords: pKa prediction, molecular docking, conformer search, chemistry workflow, drug discovery, SMILES, protein structure, batch molecular modeling, cloud chemistry openclaw: primaryEnv: ROWAN_API_KEY envVars: - name: ROWAN_API_KEY required: true description: Rowan computational chemistry API key.

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

uv pip install rowan-python

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

# Descriptors require a 3D Molecule, not a bare SMILES string.
mol = rowan.Molecule.from_smiles("CC(=O)Oc1ccccc1C(=O)O")
wf = rowan.submit_descriptors_workflow(mol, name="aspirin")
result = wf.result()

print(result.descriptors["MW"])       # 180.042 — exact mass
print(result.descriptors["SLogP"])    # 1.31
print(result.descriptors["TopoPSA"])  # 63.6 — topological PSA

If that prints without error, you're set up correctly. These values and examples were verified against rowan-python 3.1.13.

Installation

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

User and webhook management

Authentication

Set an API key via environment variable (recommended):

export ROWAN_API_KEY="your_api_key_here"

Or set directly in Python:

import rowan
rowan.api_key = "your_api_key_here"

Verify authentication:

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 validates molecule inputs and raises ValueError for an unparseable SMILES or a workflow-incompatible input type. Always use canonicalized SMILES for reproducibility.

SMILES strings versus molecule objects

Accepted input types vary by workflow in rowan-python 3.1.13. Only these common workflows accept a bare string: pKa, conformer search, membrane permeability, ADMET, LogP, macropKa, solubility, and pose-analysis MD. Most others — including descriptors, tautomer search, docking, analogue docking, BDE, NMR, and Fukui — require rowan.Molecule.from_smiles(smiles) or an RDKit Mol/RWMol. A wrong type raises ValueError before submission.

Tip: Use RDKit to validate SMILES before submission:

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

import rowan

# 1. Submit — use the specific workflow function (not the generic submit_workflow)
workflow = rowan.submit_descriptors_workflow(
    rowan.Molecule.from_smiles("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"]) # 180.042 exact mass; no result.molecular_weight property

For long-running workflows, use streaming:

for partial in workflow.stream_result(poll_interval=5):
    print(f"Complete: {partial.complete}")  # bool, not a percentage
    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.scores. Result classes differ: conformer search uses get_energies() and get_conformers() methods.
2. Raw fallback: result.data — raw dictionary from the API

Example:

result = rowan.submit_descriptors_workflow(
    rowan.Molecule.from_smiles("CCO"),
    name="ethanol",
).result()

# Convenience property (returns all descriptors):
print(result.descriptors["MW"])       # exact/monoisotopic mass
print(result.descriptors["SLogP"])
print(result.descriptors["TopoPSA"])  # usual topological PSA

# Raw data fallback:
print(result.data["descriptors"])

Note: DescriptorsResult does not have a molecular_weight property. MW is exact/monoisotopic mass, not average molecular weight. TPSA is a 3D charged-surface descriptor; use TopoPSA for the usual topological polar surface area used in drug-likeness rules.

Cache invalidation

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

result.clear_cache()
new_structures = result.get_conformers()  # Refetched for ConformerSearchResult

Projects, folders, and organization

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

Projects

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(
    rowan.Molecule.from_smiles("CCO"), name="test compound"
)

# retrieve_project takes a UUID; list_workflows scopes with parent_uuid.
project = rowan.retrieve_project(project.uuid)
workflows = rowan.list_workflows(parent_uuid=project.uuid, size=50)

Folders

# 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(parent_uuid=folder.uuid)

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:

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:

# Step 1: Find best tautomer
taut_wf = rowan.submit_tautomer_search_workflow(
    initial_molecule=rowan.Molecule.from_smiles("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

# 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, an