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 or previous-versions page 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