Rowan

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

# 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

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 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:

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:

• Submit a workflow
• Wait for completion (with optional streaming)
• 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(
    "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:

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:

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

Example:

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:

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

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

# 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:

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="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, and sequence and

structural biology) are in

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.

Access, pricing, and credits

Free-tier limits, credit consumption per workflow, and typical cost estimates are in

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.

Common errors with their fixes, and debugging tips, are in

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.