Do I need a local compute cluster to use the Rowan skill?

No, Rowan is a cloud-native platform. All intensive quantum chemistry calculations are handled on Rowan's remote servers, requiring only an API key and internet access.

How do I securely manage my Rowan API key?

The recommended method is setting the ROWAN_API_KEY as an environment variable, which the Python library automatically detects upon initialization.

Can I integrate Rowan with my existing RDKit scripts?

Yes, Rowan provides a native RDKit API that allows you to pass RDKit molecule objects directly into functions like run_pka or run_optimization.

What levels of theory are available for calculations?

Rowan supports various levels of theory including Density Functional Theory (DFT) variants like B3LYP and ωB97X, semiempirical methods like GFN2-xTB, and neural network potentials like AIMNet2.

Which AI models does Rowan support for protein structure prediction?

Rowan supports industry-leading AI models including Chai-1, Boltz-1, and Boltz-2 for protein-ligand cofolding and structure prediction.

Rowan Computational Chemistry

Name: Rowan Computational Chemistry
Author: x-cmd

byx-cmd

•

Ciencia de Datos y ML

Automates complex quantum chemistry workflows and molecular simulations through a high-level cloud-based Python API.

Rowan provides programmatic access to advanced computational chemistry, allowing researchers and developers to perform molecular property predictions, geometry optimizations, and AI-powered protein-ligand docking without requiring local high-performance computing infrastructure. By integrating state-of-the-art models like Chai-1 and Boltz for protein cofolding and AIMNet2 for neural network potentials, Rowan streamlines the path from chemical SMILES to deep physical insights, making it an essential tool for drug discovery, materials science, and automated laboratory pipelines.

Características Principales

019 GitHub stars

02Protein-ligand docking via AutoDock Vina and AI-driven structure prediction with Chai-1/Boltz models.

03Native RDKit integration for seamless cheminformatics and batch processing workflows.

04Automated molecular property prediction including pKa, solubility, and redox potential.

05Cloud-based execution with automatic resource scaling and no local software installation requirements.

06High-throughput geometry optimization and conformer searching using DFT and neural network potentials.

Casos de Uso

01Predicting protein-ligand complex structures using state-of-the-art AI cofolding models for structural biology.

02Automating the generation of minimum-energy conformers and transition state structures for synthetic planning.

03Accelerating drug discovery by predicting binding affinity and ADMET properties for large compound libraries.

Características Principales

019 GitHub stars

02Protein-ligand docking via AutoDock Vina and AI-driven structure prediction with Chai-1/Boltz models.

03Native RDKit integration for seamless cheminformatics and batch processing workflows.

04Automated molecular property prediction including pKa, solubility, and redox potential.

05Cloud-based execution with automatic resource scaling and no local software installation requirements.

06High-throughput geometry optimization and conformer searching using DFT and neural network potentials.

Casos de Uso

01Predicting protein-ligand complex structures using state-of-the-art AI cofolding models for structural biology.

02Automating the generation of minimum-energy conformers and transition state structures for synthetic planning.

03Accelerating drug discovery by predicting binding affinity and ADMET properties for large compound libraries.