# Nvmolkit Usage

Use when writing or debugging nvMolKit Python code for GPU-accelerated RDKit...

- Repository: https://github.com/NVIDIA/skills (3812 stars)
- Category: Testing
- License: Apache-2.0
- Last updated: 2026-10-30
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add NVIDIA/skills --skill nvmolkit-usage -g -a claude-code -y`
Codex: `npx skills add NVIDIA/skills --skill nvmolkit-usage -g -a codex -y`
Cursor: `npx skills add NVIDIA/skills --skill nvmolkit-usage -g -a cursor -y`
Gemini CLI: `npx skills add NVIDIA/skills --skill nvmolkit-usage -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add NVIDIA/skills`

## Description

Use when writing or debugging nvMolKit Python code for GPU-accelerated RDKit fingerprints, similarity, conformers, clustering, and molecular searches.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# nvMolKit usage

## Purpose

GPU-accelerated, batched implementations of common RDKit operations. APIs mirror RDKit where possible but are batch-oriented: they take lists of `rdkit.Chem.Mol` (or lists of fingerprints) and process them in parallel on one or more GPUs. nvMolKit links against RDKit at build time; inputs and outputs are real RDKit `Mol` objects.

This skill covers the installed Python API. Building nvMolKit from source is out of scope.

## Where nvMolKit does well

Reach for nvMolKit when:

- The workload is **a large batch of molecules** processed together (typically thousands or more).
- The metric is **throughput / total wall time across the batch**, not per-molecule latency.
- The same operation is **repeated identically** across the batch (fingerprinting a library, embedding/minimizing many conformers, bulk pairwise similarity), so the GPU stays saturated.

## Requirements

- An NVIDIA GPU with compute capability 7.0 (V100) or higher
- A CUDA driver compatible with CUDA 12.6+.
- A working `torch` install with CUDA support (nvMolKit returns GPU tensors via `torch`'s CUDA array interface).

When helping with installation, make the user choose a PyTorch CUDA backend that the host driver supports before installing nvMolKit. nvMolKit's PyPI wheels are built with CUDA Toolkit 12.9 and depend on CUDA 12 runtime packages, but pip/uv can still select a CUDA 13 PyTorch wheel unless the install command says otherwise.

- Conda: prefer conda-forge `pytorch-gpu`; pin `cuda-version=12.6` or another CUDA version supported by the driver.
- pip: send the user to the [PyTorch install selector](https://pytorch.org/get-started/locally/) or [previous-versions page](https://pytorch.org/get-started/previous-versions/) to install `torch` for a CUDA 12.x backend before installing nvMolKit.
- uv: install nvMolKit with an explicit backend, e.g. `uv pip install --torch-backend=cu128 nvmolkit`.

## Inputs

The rest: https://github.com/NVIDIA/skills/blob/HEAD/skills/bionemo-nvmolkit-usage/SKILL.md