Rowan: Cloud-Based Quantum Chemistry Platform

Dependencies

• Python: 3.10+. Repository baseline for current packaged skills.
• Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

Skill directory: 20260316/scientific-skills/Data Analytics/rowan
No packaged executable script was detected.
Use the documented workflow in SKILL.md together with the references/assets in this folder.

Example run plan:

1. Read the skill instructions and collect the required inputs.
2. Follow the documented workflow exactly.
3. Use packaged references/assets from this folder when the task needs templates or rules.
4. Return a structured result tied to the requested deliverable.

Implementation Details

See ## Overview above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: instruction-only workflow in SKILL.md.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

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 local computational resources or expertise in multiple quantum chemistry software packages.

Core Capabilities:

• Molecular property prediction (pKa, redox potential, solubility, ADMET-Tox)
• Geometry optimization and conformational search
• Protein-ligand docking based on AutoDock Vina
• AI-driven protein cofolding using Chai-1 and Boltz models
• Support for DFT, semi-empirical, and neural network potential methods
• Cloud computing with automatic resource allocation

Why Rowan:

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

Installation and Authentication

Installation

uv pip install rowan-python

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 when the module is imported.

Verify Setup

import rowan

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

Core Workflows

1. pKa Prediction

Calculates acid dissociation constant for a molecule:

import rowan
import stjames

# Create molecule from SMILES
mol = stjames.Molecule.from_smiles("c1ccccc1O")  # Phenol

# Submit pKa workflow
workflow = rowan.submit_pka_workflow(
    initial_molecule=mol,
    name="phenol pKa calculation"
)

# Wait for completion
workflow.wait_for_result()
workflow.fetch_latest(in_place=True)

# Access results
print(f"Strongest acid pKa: {workflow.data['strongest_acid']}")  # ~10.17

2. Conformational Search

Generates and optimizes molecular conformations:

import rowan
import stjames

mol = stjames.Molecule.from_smiles("CCCC")  # Butane

workflow = rowan.submit_conformer_search_workflow(
    initial_molecule=mol,
    name="butane conformer search"
)

workflow.wait_for_result()
workflow.fetch_latest(in_place=True)

# Access conformational ensemble
conformers = workflow.data['conformers']
for i, conf in enumerate(conformers):
    print(f"Conformer {i}: Energy = {conf['energy']:.4f} Hartree")

3. Geometry Optimization

Optimizes molecular geometry to energy minimum:

import rowan
import stjames

mol = stjames.Molecule.from_smiles("CC(=O)O")  # Acetic acid

workflow = rowan.submit_basic_calculation_workflow(
    initial_molecule=mol,
    name="acetic acid optimization",
    workflow_type="optimization"
)

workflow.wait_for_result()
workflow.fetch_latest(in_place=True)

# Get optimized structure
optimized_mol = workflow.data['final_molecule']
print(f"Final energy: {optimized_mol.energy} Hartree")

4. Protein-Ligand Docking

Docks small molecules into protein targets:

import rowan

# First, upload or create protein
protein = rowan.create_protein_from_pdb_id(
    name="EGFR kinase",
    code="1M17"
)

# Define binding pocket (from crystal structure or manually)
pocket = {
    "center": [10.0, 20.0, 30.0],
    "size": [20.0, 20.0, 20.0]
}

# Submit docking
workflow = rowan.submit_docking_workflow(
    protein=protein.uuid,
    pocket=pocket,
    initial_molecule=stjames.Molecule.from_smiles("Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1"),
    name="EGFR docking"
)

workflow.wait_for_result()
workflow.fetch_latest(in_place=True)

# Access docking results
docking_score = workflow.data['docking_score']
print(f"Docking score: {docking_score}")

5. Protein Cofolding (AI Structure Prediction)

Predicts protein-ligand complex structures using AI models:

import rowan

# Protein sequence
protein_seq = "MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL"

# Ligand SMILES
ligand = "CCC(C)CN=C1NCC2(CCCOC2)CN1"

# Submit cofolding using Chai-1
workflow = rowan.submit_protein_cofolding_workflow(
    initial_protein_sequences=[protein_seq],
    initial_smiles_list=[ligand],
    name="kinase-ligand cofolding",
    model="chai_1r"  # or "boltz_1x", "boltz_2"
)

workflow.wait_for_result()
workflow.fetch_latest(in_place=True)

# Access structure prediction results
print(f"Predicted TM Score: {workflow.data['ptm_score']}")
print(f"Interface pTM: {workflow.data['interface_ptm']}")

RDKit-Native API

For users working with RDKit molecules, Rowan provides a simplified interface:

import rowan
from rdkit import Chem

# Create RDKit molecule
mol = Chem.MolFromSmiles("c1ccccc1O")

# Calculate pKa directly
pka_result = rowan.run_pka(mol)
print(f"pKa: {pka_result.strongest_acid}")

# Batch processing
mols = [Chem.MolFromSmiles(smi) for smi in ["CCO", "CC(=O)O", "c1ccccc1O"]]
results = rowan.batch_pka(mols)

for mol, result in zip(mols, results):
    print(f"{Chem.MolToSmiles(mol)}: pKa = {result.strongest_acid}")

Available RDKit-native functions:

• run_pka, batch_pka - pKa calculation
• run_tautomers, batch_tautomers - Tautomer enumeration
• run_conformers, batch_conformers - Conformer generation
• run_energy, batch_energy - Single-point energy calculation
• run_optimization, batch_optimization - Geometry optimization

See references/rdkit_native.md for full documentation.

Workflow Management

Listing and Querying Workflows

# List recent workflows
workflows = rowan.list_workflows(size=10)
for wf in workflows:
    print(f"{wf.name}: {wf.status}")

# Filter by status
pending = rowan.list_workflows(status="running")

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

Batch Operations

# Submit multiple workflows
workflows = rowan.batch_submit_workflow(
    molecules=[mol1, mol2, mol3],
    workflow_type="pka",
    workflow_data={}
)

# Poll status of multiple workflows
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(
    initial_molecule=mol,
    name="compound pKa",
    folder_uuid=folder.uuid
)

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

Computational Methods

Rowan supports multiple theoretical levels:

Neural Network Potentials:

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

Semi-empirical:

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

Density Functional Theory (DFT):

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

The system automatically selects methods based on workflow type, or you can explicitly specify them in workflow parameters.

Reference Documentation

For detailed API documentation, see the following reference files:

• references/api_reference.md: Complete API documentation - Workflow class, submission functions, retrieval methods
• references/workflow_types.md: All 30+ workflow types and parameters - pKa, docking, cofolding, etc.
• references/rdkit_native.md: RDKit-native API functions for seamless chemoinformatics integration
• 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

import rowan
import stjames

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

# Submit all pKa calculations
workflows = []
for smi in smiles_list:
    mol = stjames.Molecule.from_smiles(smi)
    wf = rowan.submit_pka_workflow(
        initial_molecule=mol,
        name=f"pKa: {smi}"
    )
    workflows.append(wf)

# Wait for all to complete
for wf in workflows:
    wf.wait_for_result()
    wf.fetch_latest(in_place=True)
    print(f"{wf.name}: pKa = {wf.data['strongest_acid']}")

Pattern 2: Virtual Screening

import rowan

# Upload protein once
protein = rowan.upload_protein("target.pdb", name="Drug Target")
protein.sanitize()  # Structure cleaning

# Define pocket
pocket = {"center": [x, y, z], "size": [20, 20, 20]}

# Screen compound library
for smiles in compound_library:
    mol = stjames.Molecule.from_smiles(smiles)
    workflow = rowan.submit_docking_workflow(
        protein=protein.uuid,
        pocket=pocket,
        initial_molecule=mol,
        name=f"Dock: {smiles[:20]}"
    )

Pattern 3: Conformation-Based Analysis

import rowan
import stjames

mol = stjames.Molecule.from_smiles("complex_molecule_smiles")

# Generate conformers
conf_wf = rowan.submit_conformer_search_workflow(
    initial_molecule=mol,
    name="conformer search"
)
conf_wf.wait_for_result()
conf_wf.fetch_latest(in_place=True)

# Analyze lowest energy conformers
conformers = sorted(conf_wf.data['conformers'], key=lambda x: x['energy'])
print(f"Found {len(conformers)} unique conformers")
print(f"Energy range: {conformers[0]['energy']:.4f} to {conformers[-1]['energy']:.4f} Hartree")

Best Practices

1. Set API key via environment variable for security and convenience
2. Use folders to organize related workflows
3. Check workflow status before accessing data
4. Use batch functions for processing multiple similar calculations
5. Handle errors gracefully - workflows may fail due to invalid molecules
6. Monitor credits - check balance with rowan.whoami().credits

Error Handling

import rowan