Rowan

This is a Python API for cloud-based molecular modeling and drug discovery workflows. It handles pKa prediction, conformer generation, molecular docking, protein-ligand cofolding, and related chemistry tasks without needing your own HPC infrastructure. The credit-based pricing is straightforward: CPU costs 1 credit per minute, GPU is 3, and typical jobs like descriptor calculation run under a minute while docking might take 5 to 20 minutes. It's built for programmatic batch screening and multi-step pipelines, so if you're running medicinal chemistry campaigns or need to dock dozens of analogues without managing clusters yourself, it's worth looking at. Free tier gives you 20 credits per week to test it out.

skills.sh installs: 2K
GitHub stars: 56.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill rowan --agent claude-code

Installs into .claude/skills of the current project.

Files

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. The service manages hosted compute and results; available workflows depend on the account.

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

SKILL.md on GitHub