rowan

# 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:** - Simple molecular I/O (use RDKit directly) - Post-HF *ab initio* quantum chemistry or relativistic calculations ## Access and pricing model Rowan uses a credit-based usage model. All users, including free-tier users, can create API keys and use the Python API. ### Free-tier access - Access to...