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

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: 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}")

Webhook secret management

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

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:

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(
    "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:

1. Convenience properties (recommended first): result.descriptors, result.best_pose, result.conformer_energies
2. 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

Common workflow categories

1. Descriptors

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

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)

# 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.

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.

wf = rowan.submit_conformer_search_workflow(
    initial_molecule="CCOC(=O)N1CCC(CC1)Oc1ncnc2ccccc12",
    num_conformers=50,  # Optional: override default
    name="conformer search",
)

result = wf.result()
print(result.conformer_energies)  # [0.0, 1.2, 2.5, ...]
print(result.conformer_molecules)  # List of 3D molecules
print(result.best_conformer)  # Lowest-energy conformer

5. Tautomer search

For heterocycles and systems where tautomer state affects downstream modeling.

wf = rowan.submit_tautomer_search_workflow(
    initial_molecule="O=c1[nH]ccnc1",  # or keto tautomer
    name="imidazolone tautomers",
)

result = wf.result()
print(result.best_tautomer)  # Most stable SMILES string
print(result.tautomers)      # List of tautomeric SMILES
print(result.molecules)      # List of molecule objects

6. Docking

For protein-ligand docking with optional pose refinement and conformer generation.

# Upload protein once, reuse in multiple workflows
protein = rowan.upload_protein(
    name="CDK2",
    file_path="cdk2.pdb",
)

# Define binding pocket
pocket = {
    "center": [10.5, 24.2, 31.8],
    "size": [18.0, 18.0, 18.0],
}

# Submit docking
wf = rowan.submit_docking_workflow(
    protein=protein,
    pocket=pocket,
    initial_molecule="CCNc1ncc(c(Nc2ccc(F)cc2)n1)-c1cccnc1",
    do_pose_refinement=True,
    do_conformer_search=True,
    name="lead docking",
)

result = wf.result()
print(result.scores)  # Docking scores (kcal/mol)
print(result.best_pose)  # Mol object with 3D coordinates
print(result.data)  # Raw result dict

Protein preparation tips:

• PDB files should be reasonably clean (remove water/heteroatoms unless intended)
• Use the same protein object across a docking series for consistency
• If you have a PDB ID, use rowan.create_protein_from_pdb_id() instead

7. Analogue docking

For placing a compound series into a shared binding context.

# Analogue series (e.g., SAR campaign)
analogues = [
    "CCNc1ncc(c(Nc2ccc(F)cc2)n1)-c1cccnc1",    # reference
    "CCNc1ncc(c(Nc2ccc(Cl)cc2)n1)-c1cccnc1",   # chloro
    "CCNc1ncc(c(Nc2ccc(OC)cc2)n1)-c1cccnc1",   # methoxy
    "CCNc1ncc(c(Nc2cc(C)c(F)cc2)n1)-c1cccnc1", # methyl, fluoro
]

wf = rowan.submit_analogue_docking_workflow(
    analogues=analogues,
    initial_molecule=analogues[0],  # Reference ligand
    protein=protein,
    pocket=pocket,
    name="SAR series docking",
)

result = wf.result()
print(result.analogue_scores)  # List of scores for each analogue
print(result.best_poses)  # List of poses

8. MSA generation

For multiple-sequence alignment (useful for downstream cofolding).

wf = rowan.submit_msa_workflow(
    initial_protein_sequences=[
        "MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVP"
    ],
    output_formats=["colabfold", "chai", "boltz"],
    name="target MSA",
)

result = wf.result()
result.download_files()  # Downloads alignments to disk

9. Protein-ligand cofolding

For AI-based bound-complex prediction when no crystal structure is available.

wf = rowan.submit_protein_cofolding_workflow(
    initial_protein_sequences=[
        "MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVP"
    ],
    initial_smiles_list=[
        "CCNc1ncc(c(Nc2ccc(F)cc2)n1)-c1cccnc1"
    ],
    name="protein-ligand cofolding",
)

result = wf.result()
print(result.predictions)  # List of predicted structures
print(result.messages)  # Model metadata/warnings

predicted_structure = result.get_predicted_structure()
predicted_structure.write("predicted_complex.pdb")

All supported workflow types

All workflows follow the same submit → wait → retrieve pattern and support webhooks and project/folder organization.

Core molecular modeling workflows

Workflow	Function	When to use
Descriptors	submit_descriptors_workflow	First-pass triage: MW, LogP, TPSA, HBA/HBD, Lipinski filter
pKa	submit_pka_workflow	Single ionizable group; need protonation thermodynamics
MacropKa	submit_macropka_workflow	Multi-ionizable drugs; pH-dependent charge/LogD/solubility
Conformer Search	submit_conformer_search_workflow	3D ensemble for docking, MD, or SAR; known tautomer
Tautomer Search	submit_tautomer_search_workflow	Heterocycles, keto–enol; uncertain tautomeric form
Solubility	submit_solubility_workflow	Aqueous or solvent-specific solubility prediction
Membrane Permeability	submit_membrane_permeability_workflow	Caco-2, PAMPA, BBB, plasma permeability
ADMET	submit_admet_workflow	Broad drug-likeness and ADMET property sweep

Structure-based design workflows

Workflow	Function	When to use
Docking	submit_docking_workflow	Single ligand, known binding pocket
Analogue Docking	submit_analogue_docking_workflow	SAR series (5–100+ compounds) in a shared pocket
Batch Docking	submit_batch_docking_workflow	Fast library screening; large compound sets
Protein MD	submit_protein_md_workflow	Long-timescale dynamics; conformational sampling
Pose Analysis MD	submit_pose_analysis_md_workflow	MD refinement of a docking pose
Protein Cofolding	submit_protein_cofolding_workflow	No crystal structure; AI-predicted bound complex
Protein Binder Design	submit_protein_binder_design_workflow	De novo binder generation against a protein target

Advanced computational chemistry

Workflow	Function	When to use
Basic Calculation	submit_basic_calculation_workflow	QM/ML geometry optimization or single-point energy
Electronic Properties	submit_electronic_properties_workflow	Dipole, partial charges, HOMO-LUMO, ESP
BDE	submit_bde_workflow	Bond dissociation energies; metabolic soft-spot prediction
Redox Potential	submit_redox_potential_workflow	Oxidation/reduction potentials
Spin States	submit_spin_states_workflow	Spin-state energy ordering for organometallics/radicals
Strain	submit_strain_workflow	Conformational strain relative to global minimum
Scan	submit_scan_workflow	PES scans; torsion profiles
Multistage Optimization	submit_multistage_opt_workflow	Progressive optimization across levels of theory

Reaction chemistry

Workflow	Function	When to use
Double-Ended TS Search	submit_double_ended_ts_search_workflow	Transition state between two known structures
IRC	submit_irc_workflow	Confirm TS connectivity; intrinsic reaction coordinate

Advanced properties

Workflow	Function	When to use
NMR	submit_nmr_workflow	Predicted 1H/13C chemical shifts for structure verification
Ion Mobility	submit_ion_mobility_workflow	Collision cross-section (CCS) for MS method development
Hydrogen Bond Strength	submit_hydrogen_bond_basicity_workflow	H-bond donor/acceptor strength for formulation/solubility
Fukui	submit_fukui_workflow	Site reactivity indices for electrophilic/nucleophilic attack
Interaction Energy Decomposition	submit_interaction_energy_decomposition_workflow	Fragment-level interaction analysis

Binding free energy

Workflow	Function	When to use
RBFE/FEP	submit_relative_binding_free_energy_perturbation_workflow	Relative ΔΔG for congeneric series
RBFE Graph	submit_rbfe_graph_workflow	Build and optimize an RBFE perturbation network

Sequence and structural biology

Workflow	Function	When to use
MSA	submit_msa_workflow	Multiple sequence alignment for cofolding (ColabFold, Chai, Boltz)
Solvent-Dependent Conformers	submit_solvent_dependent_conformers_workflow	Solvation-aware conformer ensembles

Batch submission and retrieval

For libraries or analogue series, submit in a loop using the specific workflow function. The generic rowan.batch_submit_workflow() and rowan.submit_workflow() functions currently return 422 errors from the API — use the named functions (submit_descriptors_workflow, submit_pka_workflow, etc.) instead.

Submit a batch

smileses = ["CCO", "CC(=O)O", "c1ccccc1O"]
names = ["ethanol", "acetic acid", "phenol"]

workflows = [
    rowan.submit_descriptors_workflow(smi, name=name)
    for smi, name in zip(smileses, names)
]

print(f"Submitted {len(workflows)} workflows")

Poll batch status

statuses = rowan.batch_poll_status([wf.uuid for wf in workflows])
# Returns aggregate counts — not per-UUID:
# {'queued': 0, 'running': 1, 'complete': 2, 'failed': 0, 'total': 3, ...}

if statuses["complete"] == statuses["total"]:
    print("All workflows done")
elif statuses["failed"] > 0:
    print(f"{statuses['failed']} workflows failed")

Retrieve and collect results

results = []
for wf in workflows:
    try:
        result = wf.result()
        results.append(result.data)
    except rowan.WorkflowError as e:
        print(f"Workflow {wf.uuid} failed: {e}")

# Optionally aggregate into DataFrame
import pandas as pd
df = pd.DataFrame(results)

Non-blocking / fire-and-check pattern

For long-running workflows where you don't want to hold a process open, submit workflows, save their UUIDs, and check back later in a separate process.

Session 1 — submit and save UUIDs:

import rowan, json

rowan.api_key = "..."
smileses = ["CCO", "CC(=O)O", "c1ccccc1O"]

workflows = [
    rowan.submit_descriptors_workflow(smi, name=f"compound_{i}")
    for i, smi in enumerate(smileses)
]

# Save UUIDs to disk (or a database)
uuids = [wf.uuid for wf in workflows]
with open("workflow_uuids.json", "w") as f:
    json.dump(uuids, f)

print("Submitted. Check back later.")

Session 2 — check status and collect results when ready:

import rowan, json

rowan.api_key = "..."

with open("workflow_uuids.json") as f:
    uuids = json.load(f)

results = []
for uuid in uuids:
    wf = rowan.retrieve_workflow(uuid)
    if wf.done():
        result = wf.result(wait=False)
        results.append({"uuid": uuid, "data": result.data})
    else:
        print(f"{uuid}: still running ({wf.status})")

print(f"Collected {len(results)} completed results")

Webhooks and asynchronous workflows

For long-running campaigns or when you don't want to keep a process alive, use webhooks to notify your backend when workflows complete.

Setting up webhooks

Every workflow submission function accepts a webhook_url parameter:

wf = rowan.submit_docking_workflow(
    protein=protein,
    pocket=pocket,
    initial_molecule="CCO",
    webhook_url="https://myserver.com/rowan_callback",
    name="docking with webhook",
)

print(f"Workflow submitted. Result will be POSTed to webhook when complete.")

Webhook URLs can be passed to any specific workflow function (submit_docking_workflow(), submit_pka_workflow(), submit_descriptors_workflow(), etc.).

Webhook authentication with secrets

Rowan supports webhook signature verification to ensure requests are authentic. You'll need to:

1. Create or retrieve a webhook secret:

import rowan

# Create a new webhook secret
secret = rowan.create_webhook_secret()
print(f"Your webhook secret: {secret.secret}")

# Or retrieve an existing secret
secret = rowan.get_webhook_secret()

# Rotate your secret (invalidates old one, creates new)
new_secret = rowan.rotate_webhook_secret()

2. Verify incoming webhook requests:

import rowan
import hmac
import json

def verify_webhook(request_body: bytes, signature: str, secret: str) -> bool:
    """Verify the HMAC-SHA256 signature of a webhook request."""
    return rowan.verify_webhook_secret(request_body, signature, secret)

Webhook payload and signature

When a workflow completes, Rowan POSTs a JSON payload to your webhook URL with the header:

X-Rowan-Signature: <HMAC-SHA256 signature>

The request body contains the complete workflow result:

{
  "workflow_uuid": "wf_12345abc",
  "workflow_type": "docking",
  "workflow_name": "lead docking",
  "status": "COMPLETED_OK",
  "created_at": "2025-04-01T12:00:00Z",
  "completed_at": "2025-04-01T12:15:30Z",
  "data": {
    "scores": [-8.2, -8.0, -7.9],
    "best_pose": {...},
    "metadata": {...}
  }
}

Example webhook handler with signature verification (FastAPI)

from fastapi import FastAPI, Request, HTTPException
import rowan
import json

app = FastAPI()
_ws = rowan.get_webhook_secret() or rowan.create_webhook_secret()
webhook_secret = _ws.secret

@app.post("/rowan_callback")
async def handle_rowan_webhook(request: Request):
    # Get request body and signature
    body = await request.body()
    signature = request.headers.get("X-Rowan-Signature")

    if not signature:
        raise HTTPException(status_code=400, detail="Missing X-Rowan-Signature header")

    # Verify signature
    if not rowan.verify_webhook_secret(body, signature, webhook_secret):
        raise HTTPException(status_code=401, detail="Invalid webhook signature")

    # Parse and process
    payload = json.loads(body)
    wf_uuid = payload["workflow_uuid"]
    status = payload["status"]

    if status == "COMPLETED_OK":
        print(f"Workflow {wf_uuid} succeeded!")
        result_data = payload["data"]
        # Process result, update database, trigger next workflow, etc.
    elif status == "FAILED":
        print(f"Workflow {wf_uuid} failed!")
        # Handle failure

    # Respond quickly to prevent retries
    return {"status": "received"}

Webhook best practices

• Always verify signatures using rowan.verify_webhook_secret() to ensure requests are from Rowan
• Respond quickly (< 5 seconds); offload heavy processing to async tasks or background jobs
• Implement idempotency: workflows may retry; handle duplicate payloads gracefully using workflow_uuid
• Log all events for debugging and audit trails
• Use for long campaigns: webhooks shine with 50+ workflows; for small jobs, polling with result() is simpler
• Rotate secrets regularly using rowan.rotate_webhook_secret() for security
• Return 2xx status to confirm receipt; Rowan may retry on 5xx errors

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

End-to-end example: Lead optimization campaign

This example demonstrates a realistic workflow for optimizing a hit compound:

import rowan
import pandas as pd

# 1. Create a project and folder for organization
project = rowan.create_project(name="CDK2 Hit Optimization")
rowan.set_project("CDK2 Hit Optimization")
folder = rowan.create_folder(name="round_1_tautomers_and_pka")

# 2. Load hit compound and analogues
hit = "CCNc1ncc(c(Nc2ccc(F)cc2)n1)-c1cccnc1"  # Known hit
analogues = [
    "CCNc1ncc(c(Nc2ccccc2)n1)-c1cccnc1",      # Remove F
    "CCNc1ncc(c(Nc2ccc(Cl)cc2)n1)-c1cccnc1",  # Cl instead of F
    "CCC(C)Nc1ncc(c(Nc2ccc(F)cc2)n1)-c1cccnc1",  # Propyl instead of ethyl
]

# 3. Determine best tautomers (just in case)
print("Searching tautomeric forms...")
taut_workflows = [
    rowan.submit_tautomer_search_workflow(
        smi, name=f"analog_{i}", folder=folder,
    )
    for i, smi in enumerate(analogues)
]

best_tautomers = []
for wf in taut_workflows:
    result = wf.result()
    best_tautomers.append(result.best_tautomer)

# 4. Predict pKa and basic properties for all analogues
print("Predicting pKa and properties...")
pka_workflows = [
    rowan.submit_pka_workflow(
        smi, method="chemprop_nevolianis2025", name=f"pka_{i}", folder=folder,
    )
    for i, smi in enumerate(best_tautomers)
]

descriptor_workflows = [
    rowan.submit_descriptors_workflow(smi, name=f"desc_{i}", folder=folder)
    for i, smi in enumerate(best_tautomers)
]

# 5. Collect results
pka_results = []
for wf in pka_workflows:
    try:
        result = wf.result()
        pka_results.append({
            "compound": wf.name,
            "pka": result.strongest_acid,  # pKa of the strongest acid site
            "uuid": wf.uuid,
        })
    except rowan.WorkflowError as e:
        print(f"pKa prediction failed for {wf.name}: {e}")

descriptor_results = []
for wf in descriptor_workflows:
    try:
        result = wf.result()
        desc = result.descriptors
        descriptor_results.append({
            "compound": wf.name,
            "mw": desc.get("MW"),
            "logp": desc.get("SLogP"),
            "hba": desc.get("nHBAcc"),
            "hbd": desc.get("nHBDon"),
            "uuid": wf.uuid,
        })
    except rowan.WorkflowError as e:
        print(f"Descriptor calculation failed for {wf.name}: {e}")

# 6. Merge and summarize
df_pka = pd.DataFrame(pka_results)
df_desc = pd.DataFrame(descriptor_results)
df = df_pka.merge(df_desc, on="compound", how="outer")

print("\n=== Preliminary SAR ===")
print(df.to_string())

# 7. Select promising compound for docking
# compound names are "pka_0", "pka_1", etc. — extract index to look up SMILES
top_idx = int(df.loc[df["pka"].idxmin(), "compound"].split("_")[1])
top_smiles = best_tautomers[top_idx]

print(f"\nProceeding with docking: {top_smiles}")

# 8. Docking campaign
protein = rowan.create_protein_from_pdb_id(name="CDK2_1CKP", code="1CKP")
pocket = {"center": [10.5, 24.2, 31.8], "size": [18.0, 18.0, 18.0]}

docking_wf = rowan.submit_docking_workflow(
    protein=protein,
    pocket=pocket,
    initial_molecule=top_smiles,
    do_pose_refinement=True,
    name=f"docking_{top_compound}",
)

dock_result = docking_wf.result()
print(f"\nDocking score: {dock_result.scores[0]:.2f} kcal/mol")
print(f"Best pose saved to: best_pose.pdb")
dock_result.best_pose.write("best_pose.pdb")

Error handling and troubleshooting

Common errors and solutions

import rowan

# Error 1: Invalid SMILES
try:
    wf = rowan.submit_descriptors_workflow("CCCC(CC", name="bad smiles")  # Invalid
except rowan.ValidationError as e:
    print(f"Invalid SMILES: {e}")
    # Solution: Use RDKit to validate before submission
    from rdkit import Chem
    smi = Chem.MolToSmiles(Chem.MolFromSmiles(smi))

# Error 2: API key not set
try:
    wf = rowan.submit_descriptors_workflow("CCO")
except rowan.AuthenticationError:
    print("API key not found. Set ROWAN_API_KEY env var or call rowan.api_key = '...'")

# Error 3: Insufficient credits
try:
    wf = rowan.submit_protein_cofolding_workflow(...)
except rowan.InsufficientCreditsError as e:
    print(f"Not enough credits: {e}. Purchase more or reduce job size.")

# Error 4: Workflow failed (bad molecule, etc.)
try:
    wf = rowan.submit_docking_workflow(...)
    result = wf.result()
except rowan.WorkflowError as e:
    print(f"Workflow failed: {e}")
    # Check wf.status for details
    print(f"Status: {wf.status}")

# Error 5: Workflow not yet done — poll manually
result = wf.result(wait=True, poll_interval=5)  # waits and polls every 5s
# Or check status without blocking:
if not wf.done():
    print("Workflow still running. Call wf.result() again later.")

Debugging tips

• Check workflow status: wf.status, check wf.done(), or call wf.get_status()
• Inspect raw result: result.data instead of convenience properties
• Re-run failed workflow: Save UUIDs and retry with rowan.retrieve_workflow(uuid)
• Validate molecules beforehand: Use RDKit or Chemaxon before batch submission

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.