Automates complex quantum chemistry workflows and molecular simulations via a cloud-based Python API.
Rowan is a specialized computational chemistry skill that enables programmatic access to sophisticated molecular simulations and property predictions without the need for local high-performance computing clusters. It streamlines tasks such as pKa prediction, geometry optimization, and conformer searching, while integrating cutting-edge AI models like Chai-1 and Boltz-1/2 for protein cofolding. By providing a unified Python API for DFT, semiempirical methods, and neural network potentials like AIMNet2, Rowan allows researchers to build automated drug discovery and chemical analysis pipelines directly within their development environment.
主要功能
01Automated geometry optimization and conformer ensemble generation
02Cloud-based compute resources with no local installation required
03RDKit-native API for seamless cheminformatics integration
041 GitHub stars
05High-accuracy pKa and molecular property prediction
06AI-powered protein-ligand cofolding and docking (Chai-1, Boltz-1/2, Vina)
使用场景
01Automating virtual screening for drug discovery projects
02Predicting chemical properties for synthesis planning and ADMET-Tox screening
03Modeling 3D protein-ligand interactions using state-of-the-art AI models
