rowan

SKILL.md

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