Rowan Agent Skill

Cloud-based quantum chemistry platform with Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Use when tasks involve quantum chemistry calculations, molecular property prediction, DFT or semiempirical methods, neural network potentials (AIMNet2), protein-ligand binding predictions, or automated computational chemistry pipelines. Provides cloud compute resources with no local setup required.

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Install

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npx skills add https://github.com/foryourhealth111-pixel/Vibe-Skills --skill rowan

What comes with it

62 060 bytes besides the instruction

references/api_reference.md

references/molecule_handling.md

references/proteins_and_organization.md

references/rdkit_native.md

references/results_interpretation.md

references/workflow_types.md

The instruction itself

27 sections, as written by the author

Rowan: Cloud-Based Quantum Chemistry Platform

Routing Boundary

Use this skill only for explicit Rowan context, including Rowan, rowan-python, labs.rowansci.com, the Rowan API, or a Rowan-specific chemistry workflow. Do not use it for generic chemistry, RDKit, PubChem, ChEMBL, docking, pKa, conformer search, quantum chemistry, molecular machine learning, Boltz, or Chai-1 unless explicit Rowan context is present.

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

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 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}")

Core Workflows

1. pKa Prediction

Calculate the acid dissociation constant for molecules:

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. Conformer Search

Generate and optimize molecular conformers:

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

3. Geometry Optimization

Optimize molecular geometry to minimum energy structure:

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

Dock small molecules to 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 manual)
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)

Predict 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 with 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 predictions
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")

# Compute 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 calculations
run_tautomers, batch_tautomers - Tautomer enumeration
run_conformers, batch_conformers - Conformer generation
run_energy, batch_energy - Single-point energies
run_optimization, batch_optimization - Geometry optimization

See references/rdkit_native.md for complete documentation.

Workflow Management

List and Query 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")

# Retrieve 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 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: Complete API documentation - Workflow class, submission functions, retrieval methods
references/workflow_types.md: All 30+ workflow types with parameters - pKa, docking, cofolding, etc.
references/rdkit_native.md: RDKit-native API functions for seamless cheminformatics 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()  # Clean structure

# 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: Conformer-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

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

Error Handling

import rowan

try:
    workflow = rowan.submit_pka_workflow(
        initial_molecule=mol,
        name="calculation"
    )
    workflow.wait_for_result(timeout=3600)  # 1 hour timeout

    if workflow.status == "completed":
        workflow.fetch_latest(in_place=True)
        print(workflow.data)
    elif workflow.status == "failed":
        print(f"Workflow failed: {workflow.error_message}")

except rowan.RowanAPIError as e:
    print(f"API error: {e}")
except TimeoutError:
    print("Workflow timed out")

Additional Resources

Web Interface: https://labs.rowansci.com
Documentation: https://docs.rowansci.com
Python API Docs: https://docs.rowansci.com/api/python/v2/
Tutorials: https://docs.rowansci.com/tutorials

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