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 majiayu000/claude-skill-registry --skill rowan-k-dense-ai-scientific-agent-ski-2 --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-native workflow platform for molecular simulation, medicinal chemistry, and structure-based design. It exposes a Python API to run small-molecule modeling, property prediction, docking, molecular dynamics, and AI structure workflows, without local HPC or GPU provisioning. It is intended for programmatic batch screening and multi-step chemistry pipelines.
How it works
- Submit a specific workflow via functions like submit_descriptors_workflow, submit_pka_workflow, submit_macropka_workflow, submit_conformer_search_workflow, submit_tautomer_search_workflow, submit_docking_workflow, etc.
- Wait for completion using result() or streaming with stream_result().
- Retrieve typed results through properties such as result.descriptors, result.best_pose, result.conformer_energies, result.best_tautomer, result.pka_values, etc., or via result.data for raw API dictionaries.
- Manage inputs in various formats (SMILES, InChI, etc.) and organize tasks in projects or folders if needed.
When to use it
Use Rowan for batch property prediction (pKa, descriptors, permeability), conformer/tautomer ensemble generation, docking workflows (single ligand, analogue docking), protein-ligand cofolding, MSA generation, and multi-step chemistry pipelines. Suitable when you want scalable infrastructure and API-driven workflows instead of maintaining local HPC/GPU resources.
What it can touch
- Core workflows via Python API (e.g., submit_descriptors_workflow, submit_pka_workflow, submit_macropka_workflow, submit_conformer_search_workflow, submit_docking_workflow, etc.).
- Results accessed through typed properties (descriptors, conformer_energies, best_pose, pka_values, etc.) and raw result.data dictionaries.
- Inputs include SMILES and other molecule representations; can manage projects and folders for organization.
Caveats
- License/integration notes indicate Proprietary with API key required; compatibility requires Python 3.12+ and an API key.
- Credits-based usage model with quantified runtimes and credits per minute for CPU/GPU workflows; pricing and credit consumption are documented.
- Requires an API key and appropriate access; cloud-based processing implies dependency on Rowan service availability.
# 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 majiayu000/claude-skill-registry --skill rowan-k-dense-ai-scientific-agent-ski-2 --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 majiayu000/claude-skill-registry, a repository with 534 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.