Rowan: Cloud-Based Quantum Chemistry Platform

Overview

Rowan is a cloud-based computational chemistry platform that provides programmatic access to quantum chemistry workflows through a Python API. It enables automation of complex molecular simulations without requiring local computational resources or expertise in multiple quantum chemistry packages.

Key Capabilities:

• Molecular property prediction (pKa, redox potential, solubility, ADMET-Tox)
• Geometry optimization and conformer searching
• Protein-ligand docking with AutoDock Vina
• AI-powered protein cofolding with Chai-1 and Boltz models
• Access to DFT, semiempirical, and neural network potential methods
• Cloud compute with automatic resource allocation

Why Rowan:

• No local compute cluster required
• Unified API for dozens of computational methods
• Results viewable in web interface at labs.rowansci.com
• Automatic resource scaling

Installation and Authentication

Installation

Requires Python >= 3.12. This skill targets rowan-python 3.x (the current major version; v2 had a different result API).

uv pip install "rowan-python>=3.0"

Installing rowan-python also pulls in stjames (molecule/result models) and rdkit.

Authentication

Generate an API key at labs.rowansci.com/account/api-keys.

Option 1: Direct assignment

import rowan
rowan.api_key = "your_api_key_here"

Option 2: Environment variable (recommended)

export ROWAN_API_KEY="your_api_key_here"

The API key is automatically read from ROWAN_API_KEY on module import.

Verify Setup

import rowan

# Check authentication
user = rowan.whoami()
print(f"Logged in as: {user.username}")
print(f"Credits available: {user.credits}")

The Result Pattern (read this first)

Every submit_*_workflow returns a Workflow. Do NOT read workflow.data[...] by hand and do NOT call the deprecated wait_for_result(). The v3 idiom is a single call:

workflow = rowan.submit_pka_workflow("c1ccccc1O", name="phenol pKa")
result = workflow.result()        # blocks until done, returns a typed WorkflowResult
print(result.strongest_acid)      # typed attribute access, not a dict key

Key facts:

• workflow.result(wait=True, poll_interval=5) blocks, fetches, and raises rowan.WorkflowError if the workflow failed or was stopped. Use wait=False to grab whatever is ready without blocking.
• workflow.status is the integer enum stjames.Status (QUEUED=0, RUNNING=1, COMPLETED_OK=2, FAILED=3, STOPPED=4), not a string. Use workflow.done() / workflow.is_finished() rather than comparing to "completed".
• submit_* functions accept a SMILES string, an stjames.Molecule, or an RDKit Mol directly as initial_molecule — you rarely need to build a molecule first. stjames.Molecule.from_smiles(smiles) takes only the SMILES (no charge=/multiplicity= kwargs).

Core Workflows

1. pKa Prediction

Predict micro-pKa / acid dissociation constants:

import rowan

# initial_molecule accepts a SMILES string directly
workflow = rowan.submit_pka_workflow(
    "c1ccccc1O",  # Phenol
    name="phenol pKa calculation",
    pka_range=(2, 12),                  # default
    method="aimnet2_wagen2024",         # default NNP-based pKa model
)

result = workflow.result()
print(f"Strongest acid pKa: {result.strongest_acid}")
print(f"Strongest base pKa: {result.strongest_base}")

For macroscopic pKa, microstate populations vs. pH, isoelectric point, and logD/solubility-vs-pH, use rowan.submit_macropka_workflow(...) and read result.pka_values, result.microstates, result.isoelectric_point.

2. Conformer Search

Generate and rank a conformer ensemble:

import rowan

workflow = rowan.submit_conformer_search_workflow(
    "CCCC",  # Butane
    name="butane conformer search",
    final_method="aimnet2_wb97md3",     # NNP; default
)

result = workflow.result()
print(f"Found {result.num_conformers} conformers")
for energy in result.get_energies():   # relative energies, kcal/mol
    print(f"  ΔE = {energy:.2f} kcal/mol")
lowest = result.get_conformer(0)       # stjames.Molecule of the lowest-energy conformer

3. Geometry Optimization

submit_basic_calculation_workflow is task-driven: pass tasks (e.g. ["optimize"], ["energy"], ["optimize", "frequencies"]), not a workflow_type string.

import rowan

workflow = rowan.submit_basic_calculation_workflow(
    "CC(=O)O",  # Acetic acid
    tasks=["optimize"],
    preset="organic_nnp",     # quick NNP preset; or set method=/basis_set= explicitly
    name="acetic acid optimization",
)

result = workflow.result()
print(f"Final energy: {result.energy} Hartree")
optimized_mol = result.molecule   # stjames.Molecule with optimized coordinates

4. Protein-Ligand Docking

Dock small molecules to protein targets. The pocket is [[center_x, center_y, center_z], [size_x, size_y, size_z]] in Angstroms — a list of two 3-vectors, NOT a dict.

import rowan

# Create protein from a PDB ID (fetched from RCSB)
protein = rowan.create_protein_from_pdb_id(name="EGFR kinase", code="1M17")
protein.sanitize()   # strip waters/ions, fix residues

pocket = [[10.0, 20.0, 30.0],    # center (Å)
          [20.0, 20.0, 20.0]]    # box size (Å)

workflow = rowan.submit_docking_workflow(
    protein=protein,             # Protein object or its .uuid
    pocket=pocket,
    initial_molecule="Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1",
    scoring_function="vinardo",  # or "vina"
    name="EGFR docking",
)

result = workflow.result()
best = result.scores[0]          # DockingScore, sorted best-first
print(f"Best docking score: {best.score} kcal/mol")
best_pose = result.best_pose     # stjames.Molecule of the top pose

5. Protein Cofolding (AI Structure Prediction)

Predict protein-ligand complex structures using AI models:

import rowan

protein_seq = "MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL"
ligand = "CCC(C)CN=C1NCC2(CCCOC2)CN1"

workflow = rowan.submit_protein_cofolding_workflow(
    initial_protein_sequences=[protein_seq],
    initial_smiles_list=[ligand],
    name="kinase-ligand cofolding",
    model="chai_1r",   # or "boltz_1", "boltz_2", "openfold_3"
)

result = workflow.result()
top = result.predictions[0]            # first CofoldingResult sample
print(f"pTM: {top.scores.ptm}")        # predicted TM score (0-1)
print(f"interface pTM: {top.scores.iptm}")

Note: the cofolding model strings are chai_1r, boltz_1, boltz_2, openfold_3 (there is no boltz_1x). Confidence lives on result.scores / each prediction's .scores as .ptm and .iptm.

Workflow Management

List and Query Workflows

# List recent workflows (page is 0-indexed; default size=10)
workflows = rowan.list_workflows(size=10)
for wf in workflows:
    print(f"{wf.name}: {wf.status.name}")   # status is an int enum

# Filter by type / name substring / folder
pka_runs = rowan.list_workflows(workflow_type="pka", name_contains="phenol")
folder_runs = rowan.list_workflows(parent_uuid=folder.uuid)

# Retrieve specific workflow
workflow = rowan.retrieve_workflow("workflow-uuid")

Batch Operations

# Submit many workflows of one type at once
workflows = rowan.batch_submit_workflow(
    workflow_type="pka",
    initial_smileses=["CCO", "CC(=O)O", "c1ccccc1O"],
)

# Non-blocking status poll (returns a list of {uuid, status, ...} dicts)
statuses = rowan.batch_poll_status([wf.uuid for wf in workflows])

Folder Organization

# Create folder for project
folder = rowan.create_folder(name="Drug Discovery Project")

# Submit workflow to folder
workflow = rowan.submit_pka_workflow(
    "CCO",
    name="compound pKa",
    folder=folder,          # or folder_uuid=folder.uuid
)

# List workflows in folder
folder_workflows = rowan.list_workflows(parent_uuid=folder.uuid)

Computational Methods

Rowan supports multiple levels of theory:

Neural Network Potentials:

• AIMNet2 (ωB97M-D3) - Fast and accurate
• Egret - Rowan's proprietary model

Semiempirical:

• GFN1-xTB, GFN2-xTB - Fast for large molecules

DFT:

• B3LYP, PBE, ωB97X variants
• Multiple basis sets available

Methods are automatically selected based on workflow type, or can be specified explicitly in workflow parameters.

Reference Documentation

For detailed API documentation, consult these reference files:

• `references/api_reference.md`: Workflow class, submission functions, retrieval methods, the result pattern
• `references/workflow_types.md`: The full set of workflow types with parameters - pKa, docking, cofolding, etc.
• `references/molecule_handling.md`: stjames.Molecule class - creating molecules from SMILES, XYZ, RDKit
• `references/proteins_and_organization.md`: Protein upload, folder management, project organization
• `references/results_interpretation.md`: Understanding workflow outputs, confidence scores, validation

Common Patterns

Pattern 1: Property Prediction Pipeline

Submit everything first, then collect results — submission is non-blocking, result() blocks.

import rowan

smiles_list = ["CCO", "c1ccccc1O", "CC(=O)O"]

# Submit all pKa calculations (SMILES strings are accepted directly)
workflows = [rowan.submit_pka_workflow(smi, name=f"pKa: {smi}") for smi in smiles_list]

# Collect results
for wf in workflows:
    result = wf.result()
    print(f"{wf.name}: pKa = {result.strongest_acid}")

Pattern 2: Virtual Screening

For screening a library against one target, prefer the dedicated batch-docking workflow over a Python loop.

import rowan

protein = rowan.upload_protein(name="Drug Target", file_path="target.pdb")
protein.sanitize()

pocket = [[x, y, z], [20.0, 20.0, 20.0]]   # center, size (Å)

workflow = rowan.submit_batch_docking_workflow(
    smiles_list=compound_library,
    protein=protein,
    pocket=pocket,
    name="library screen",
)
result = workflow.result()

Pattern 3: Conformer-Based Analysis

import rowan

conf_wf = rowan.submit_conformer_search_workflow(
    "C1CCCCC1",  # any SMILES
    name="conformer search",
)
result = conf_wf.result()

energies = result.get_energies()   # relative energies, kcal/mol, ascending
print(f"Found {result.num_conformers} conformers")
print(f"Energy range: {energies[0]:.2f} to {energies[-1]:.2f} kcal/mol")

Best Practices

1. Set API key via environment variable for security and convenience
2. Use folders to organize related workflows
3. Use `workflow.result()` — it waits, fetches, and raises on failure in one call
4. Use batch functions (batch_submit_workflow, submit_batch_docking_workflow) for many similar jobs
5. Cap spend with `max_credits=` on any submission, and check rowan.whoami().credits

Error Handling

workflow.result() raises rowan.WorkflowError if the workflow failed or was stopped, so wrap it:

import rowan

workflow = rowan.submit_pka_workflow("c1ccccc1O", name="calculation", max_credits=10)

try:
    result = workflow.result()       # blocks until done; raises on failure
    print(result.strongest_acid)
except rowan.WorkflowError as e:
    # workflow failed/stopped — inspect workflow.logfile for details
    print(f"Workflow failed: {e}")
    print(workflow.logfile)

workflow.status is the int enum stjames.Status; check workflow.done() for a non-blocking finished test.

Additional Resources

• Web Interface: https://labs.rowansci.com
• Documentation: https://docs.rowansci.com
• Tutorials: https://docs.rowansci.com/tutorials