| 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: "1.1" 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 โ docking โ 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 | 0.5โ2 | Lightweight, good for triage |
| pKa (single transition) | 2โ5 min | 2โ5 | Depends on molecule size |
| MacropKa (pH 0โ14) | 5โ15 min | 5โ15 | Broader sampling, higher cost |
| Conformer search | 3โ10 min | 3โ10 | Ensemble quality matters |
| Tautomer search | 2โ5 min | 2โ5 | Heterocyclic systems |
| Docking (single ligand) | 5โ20 min | 5โ20 | Depends on pocket size, refinement |
| Analogue docking series (10โ50 ligands) | 30โ120 min | 30โ100+ | Shared reference frame |
| MSA generation | 5โ30 min | 5โ30 | Sequence length dependent |
| Protein-ligand cofolding | 15โ60 min | 20โ50+ | AI structure prediction, GPU-heavy |
Quick start
uv pip install rowan-python
import rowan
rowan.api_key = "your_api_key_here"
wf = rowan.submit_descriptors_workflow("CC(=O)Oc1ccccc1C(=O)O", name="aspirin")
result = wf.result()
print(result.descriptors['MW'])
print(result.descriptors['SLogP'])
print(result.descriptors['TPSA'])
If that prints without error, you're set up correctly.
Installation
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()
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
secret = rowan.get_webhook_secret()
if secret is None:
secret = rowan.create_webhook_secret()
print(f"Secret key: {secret.secret}")
new_secret = rowan.rotate_webhook_secret()
print(f"New secret created (old secret disabled): {new_secret.secret}")
is_valid = rowan.verify_webhook_secret(
request_body=b"...",
signature="X-Rowan-Signature",
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:
- Submit a workflow
- Wait for completion (with optional streaming)
- Retrieve typed results with convenience properties
import rowan
workflow = rowan.submit_descriptors_workflow(
"CC(=O)Oc1ccccc1C(=O)O",
name="aspirin descriptors",
)
result = workflow.result()
print(result.data)
print(result.descriptors['MW'])
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 โ raw dictionary from the API
Example:
result = rowan.submit_descriptors_workflow(
"CCO",
name="ethanol",
).result()
print(result.descriptors['MW'])
print(result.descriptors['SLogP'])
print(result.descriptors['TPSA'])
print(result.data['descriptors'])
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
Projects, folders, and organization
For nontrivial campaigns, use projects and folders to keep work organized.
Projects
import rowan
project = rowan.create_project(name="CDK2 lead optimization")
rowan.set_project("CDK2 lead optimization")
wf = rowan.submit_descriptors_workflow("CCO", name="test compound")
project = rowan.retrieve_project("CDK2 lead optimization")
workflows = rowan.list_workflows(project=project, size=50)
Folders
folder = rowan.create_folder(name="docking/batch_1/screening")
wf = rowan.submit_docking_workflow(
folder=folder,
name="compound_001",
)
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 acidโbase 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., 0โ14)
- 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 ~9โ10): 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, ketoโenol systems)
- You need to model all relevant protonation isomers
- Downstream calculations (docking, pKa) depend on tautomeric form
Combined workflow:
taut_wf = rowan.submit_tautomer_search_workflow(
initial_molecule="O=c1[nH]ccnc1",
name="imidazole tautomers",
)
best_taut = taut_wf.result().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 | 5โ100+ 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",
name="aspirin descriptors",
)
result = wf.result()
print(result.descriptors['MW'])
print(result.descriptors['SLogP'])
print(result.descriptors['TPSA'])
print(result.data['descriptors'])
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 (ร
ยฒ) | <140 for oral bioavailability |
nHBDon | H-bond donor count | โค5 (Lipinski) |
nHBAcc | H-bond acceptor count | โค10 (Lipinski) |
nRot | Rotatable bond count | <10 for oral drugs |
nRing | Ring count | โ |
nHeavyAtom | Heavy atom count | โ |
FilterItLogS | Estimated aqueous solubility (LogS) | >-4 preferred |
Lipinski | Lipinski Ro5 pass (1.0) or fail (0.0) | โ |
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) |
wf = rowan.submit_pka_workflow(
initial_molecule="c1ccccc1O",
method="starling",
name="phenol pKa",
)
result = wf.result()
print(result.strongest_acid)
print(result.conjugate_bases)
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",
min_pH=0,
max_pH=14,
min_charge=-2,
max_charge=2,
compute_aqueous_solubility=True,
name="imidazole macropKa",
)
result = wf.result()
print(result.pka_values)
print(result.logd_by_ph)
print(result.aqueous_solubility_by_ph)
print(result.isoelectric_point)
print(result.data)
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,
name="conformer search",
)
result = wf.result()
print(result.conformer_energies)
print(result.conformer_molecules)
print(result.best_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",
name="imidazolone tautomers",
)
result = wf.result()
print(result.best_tautomer)
print(result.tautomers)
print(result.molecules)
6. Docking
For protein-ligand docking with optional pose refinement and conformer generation.
protein = rowan.upload_protein(
name="CDK2",
file_path="cdk2.pdb",
)
pocket = {
"center": [10.5, 24.2, 31.8],
"size": [18.0, 18.0, 18.0],
}
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)
print(result.best_pose)
print(result.data)
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.
analogues = [
"CCNc1ncc(c(Nc2ccc(F)cc2)n1)-c1cccnc1",
"CCNc1ncc(c(Nc2ccc(Cl)cc2)n1)-c1cccnc1",
"CCNc1ncc(c(Nc2ccc(OC)cc2)n1)-c1cccnc1",
"CCNc1ncc(c(Nc2cc(C)c(F)cc2)n1)-c1cccnc1",
]
wf = rowan.submit_analogue_docking_workflow(
analogues=analogues,
initial_molecule=analogues[0],
protein=protein,
pocket=pocket,
name="SAR series docking",
)
result = wf.result()
print(result.analogue_scores)
print(result.best_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()
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)
print(result.messages)
predicted_structure = result.get_predicted_structure()
predicted_structure.write("predicted_complex.pdb")
All supported workflow types
All workflows follow the same submit โ wait โ 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, ketoโenol; 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 (5โ100+ 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 ฮฮG 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 โ 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])
if statuses["complete"] == statuses["total"]:
print("All workflows done")
elif statuses["failed"] > 0:
print(f"{statuses['failed']} workflows failed")
Retrieve and collect results
results
