rowan
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.
npx skills add BioTender-max/awesome-bio-agent-skills --skill rowan --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Rowan provides a cloud-based workflow platform for molecular modeling, medicinal chemistry, and structure-based design. It exposes a Python API to run small-molecule modeling, property prediction (e.g., pKa, descriptors, permeability), docking, molecular dynamics, and AI structure workflows. It is intended for batch workflows and pipelines that avoid local HPC/GPU infrastructure.
How it works
The skill instructs the agent to:
- Install or import the Rowan Python library and set an API key via rowan.api_key or ROWAN_API_KEY.
- Submit workflows using specific functions (e.g., submit_descriptors_workflow, submit_pka_workflow, submit_macropka_workflow, submit_conformer_search_workflow, submit_tautomer_search_workflow, submit_docking_workflow).
- Retrieve results with result() (and optionally stream_result() for long runs).
- Access results through typed properties such as result.descriptors, result.best_pose, result.conformer_energies, result.best_tautomer, and result.data as a raw dictionary.
- Use helper utilities to manage authentication, webhook secrets, and project/folder organization (create_project, set_project, create_folder, upload_protein).
- Validate input molecules via SMILES, SMARTS, or InChI and optionally validate SMILES with RDKit before submission.
- Follow the common workflow pattern: Submit → Wait/stream → Retrieve typed results.
- For results-heavy workflows, refresh caches with result.clear_cache() to refetch data like conformer geometries or protein structures.
When to use Rowan
Rowan is suited for batch property prediction (pKa, descriptors, permeability), conformer and tautomer ensemble generation, docking (single ligand, analogue docking, or protein-ligand cofolding), MSA generation, molecular dynamics, and related small-molecule or protein modeling tasks. It targets workflows that would otherwise require maintaining local HPC/GPU infrastructure and supports multi-step chemistry pipelines.
What it can touch
The skill references the following interactions and inputs:
- API key management via environment or Python API
- Submitting workflows via functions like submit_descriptors_workflow, submit_pka_workflow, submit_macropka_workflow, submit_conformer_search_workflow, submit_tautomer_search_workflow, submit_docking_workflow
- Accessing results via result() and properties like result.descriptors, result.data
- Managing projects and folders via create_project, set_project, create_folder, list_workflows
- Uploading proteins via upload_protein
- Validation of molecules via SMILES, SMARTS, InChI and optional RDKit validation
Caveats
The skill requires a API key-based access model. Licensing is Proprietary (API key required). Compatibility specifies Python 3.12+ and API key required. The license field in the skill metadata is Proprietary (API key required).
# 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:** - Sim
- Overview
- When to use Rowan
- Access and pricing model
- Free-tier access
- Pricing and credit consumption
- Typical cost estimates
- Quick start
- Installation
- User and webhook management
- Authentication
- Webhook secret management
- Molecule input formats
- Core usage pattern
- result() vs. streamresult()
uv pip install rowan-python export ROWAN_API_KEY="your_api_key_here"
What does the rowan skill do?
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.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill rowan --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.