rowan

Name: rowan
Availability: InStock
Author: shushuzn

Run cloud molecular modeling and medicinal-chemistry workflows via a Python API client.

4|Updated Mar 2, 2026

Search Tags:#medicinal chemistry #docking #molecular modeling #pka prediction #cloud API #conformer ensemble #protein-ligand cofolding

Ready to run

npx skills add https://github.com/shushuzn/Rairos --skill rowan-shushuzn

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: rowan
Source: https://github.com/shushuzn/Rairos/tree/main/skills/rowan
Command: npx skills add https://github.com/shushuzn/Rairos --skill rowan-shushuzn

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Rowan removes the overhead of setting up local HPC/GPU infrastructure by providing a unified cloud workflow API for molecular modeling and drug-design tasks you would otherwise assemble from many separate tools.

Core Features & Use Cases

Batch medicinal-chemistry pipelines: Run repeatable, multi-step workflows like tautomer search followed by docking and pose analysis with consistent infrastructure and result management.
Drug-property prediction at scale: Compute molecular descriptors and properties such as pKa, macropKa, solubility, and permeability for many candidate structures programmatically.
Structure-based design workflows: Perform protein-ligand docking, analogue docking series, protein-ligand cofolding, and MSA generation for downstream cofolding-oriented tasks.

Quick Start

Submit a descriptors workflow for a SMILES string and instantly retrieve computed descriptor values back in your Python workflow context.

Frequently Asked Questions about rowan

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I run molecular docking and pKa prediction without setting up local HPC infrastructure?▼

You can run molecular docking and pKa prediction in the cloud by submitting chemical structures and protein inputs via a Python API client, avoiding local HPC setup entirely while retrieving scalable workflow results programmatically.

Can I automate batch medicinal-chemistry workflows for large-scale compound screening?▼

Yes, batch medicinal-chemistry workflows support programmatic screening of many candidate structures, enabling repeatable multi-step pipelines like tautomer search, docking, and pose analysis with consistent cloud infrastructure and result management.

What is protein-ligand cofolding and how does it support structure-based drug design?▼

Protein-ligand cofolding is a structure-based design workflow that processes protein inputs alongside ligands and generates MSA sequences, enabling downstream cofolding-oriented tasks for drug discovery campaigns without local GPU resources.

Do I need a Python API client and API key to perform cloud-based molecular modeling?▼

Yes, accessing cloud-based molecular modeling workflows requires a Python API client configured with a valid API key to submit inputs like SMILES strings and retrieve computed descriptor values or docking outputs.

What is the best way to generate conformer ensembles and compute molecular descriptors for drug candidates?▼

The best way to generate conformer ensembles and compute molecular descriptors is using a unified cloud workflow API, which programmatically processes SMILES strings to deliver properties like solubility, permeability, and macropKa at scale.

Are there limitations to using cloud APIs for multi-step medicinal-chemistry pipelines?▼

Cloud API workflows for medicinal-chemistry pipelines are limited to supported tasks like docking, cofolding, and descriptor calculations, and they require consistent network access and valid API keys rather than local HPC offline execution.