Automate molecular modeling and medicinal chemistry workflows through a Python API.
npx skills add https://github.com/galeep/plugin-place --skill rowan-galeep
Please help me install this Agent Skill. Skill: rowan Source: https://github.com/galeep/plugin-place/tree/main/plugins/sci-cheminformatics-drug-discovery/skills/rowan Command: npx skills add https://github.com/galeep/plugin-place --skill rowan-galeep
This Skill solves the complexity and infrastructure challenges of running molecular modeling and medicinal chemistry workflows. It allows users to execute tasks like pKa prediction, docking, and molecular dynamics without the need for local HPC infrastructure.
Install Rowan with 'uv pip install rowan-python' and run a pKa prediction with 'rowan.api_key = "your_api_key_here"'. Submit a pKa workflow using 'rowan.submit_pka_workflow(smiles="CC(=O)Oc1ccccc1C(=O)O", name="aspirin")'.
High-intent search queries and answers about installing and using this skill.
Molecular modeling and docking workflows can run without local HPC infrastructure by using a cloud-native Python API that handles scalable computation and result management remotely.
You can predict pKa for aspirin in computational chemistry by installing the Python package, setting your API key, and submitting a workflow with the molecule's SMILES string.
An API key is required to access cloud-native drug discovery workflows, ensuring authorized entry to the platform and tracking computational usage via credits.
With a cloud computing platform, you can automate medicinal chemistry tasks including pKa prediction, molecular docking, conformer searches, and protein-ligand cofolding.
Molecular modeling without local GPUs works for protein-ligand interactions by offloading heavy computational tasks to a cloud-native platform via Python API calls.
The best way to streamline computational chemistry workflows for drug discovery is using a cloud-native Python API to automate tasks like docking and pKa prediction without local hardware.
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