TMol provides all-atom scoring, packing, minimization, and FastRelax in PyTorch. It batches proteins, nucleic acids, ligands, and complexes on CPU or CUDA, with gradients through coordinate scoring.
Documentation · Tutorials · API reference
Install
Install from PyPI. For GPU use, first install PyTorch with the matching CUDA major, then choose one command:
python -m pip install tmol # CPU, including Apple Silicon
python -m pip install "tmol[cu12]" # CUDA 12-enabled PyTorch
python -m pip install "tmol[cu13]" # CUDA 13-enabled PyTorch
The base package contains the Python API, data, and CPU kernels. GPU extras add
tmol-ops-cu12 or tmol-ops-cu13 at the same version. GPU use also needs a
compatible NVIDIA driver; pip does not detect GPUs or select a PyTorch CUDA build.
The installation guide
covers requirements, verification, upgrades, and source builds. TMol wheels work
across supported PyTorch minor versions; no per-Torch wheel selection is needed.
import torch, tmol
print(tmol.__version__, tmol.has_cuda(), torch.cuda.is_available())
Quick start
Score a structure on CPU. With matching CUDA-enabled PyTorch and TMol operators,
use torch.device("cuda"):
import torch
import tmol
device = torch.device("cpu")
pose = tmol.pose_stack_from_file("input.pdb", device)
score_function = tmol.beta2016_score_function(device)
score = score_function.render_whole_pose_scoring_module(pose)
print(score(pose.coords))
See the quickstart for scoring, minimization, repacking, FastRelax, batching, and ligand preparation. The visual glossary explains the concepts, and the score playground lets you rotate a side chain and inspect TMol scores in the browser. The tutorials are runnable notebooks, each also available in Colab.
Development
git clone https://github.com/uw-ipd/tmol.git
cd tmol
python -m pip install "torch>=2.14" "scikit-build-core>=0.10" "cmake>=3.24,<4" "pybind11>=2.12" ninja packaging
python -m pip install --no-build-isolation -e ".[dev]"
The development guide covers builds, tests, benchmarks, and releases. See the contributor guide for code and documentation conventions, or agent skills for reusable coding-agent instructions.
Citation
If you use TMol in your work, please cite:
Andrew Leaver-Fay, Jeff Flatten, Alex Ford, Joseph Kleinhenz, Henry Solberg, David Baker, Andrew M. Watkins, Brian Kuhlman, Frank DiMaio, tmol: a GPU-accelerated, PyTorch implementation of Rosetta's relax protocol (manuscript in preparation).
TMol is available under the terms in LICENSE.
1 maintainer
Metadata
1 maintainer
Release files for tmol 0.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| tmol-0.2.0.tar.gz | 71.6 MB | Without provenance | Details |
Built distributions (wheels)
| File | Reset | |||
|---|---|---|---|---|
| tmol-0.2.0-cp314-cp314-manylinux_2_28_x86_64.whl | CPython 3.14 | CPython 3.14 | Linux glibc 2.28+ x86-64 | Details |
| tmol-0.2.0-cp314-cp314-manylinux_2_28_aarch64.whl | CPython 3.14 | CPython 3.14 | Linux glibc 2.28+ ARM64 | Details |
| tmol-0.2.0-cp314-cp314-macosx_14_0_arm64.whl | CPython 3.14 | CPython 3.14 | macOS 14.0+ ARM64 | Details |
| tmol-0.2.0-cp313-cp313-manylinux_2_28_x86_64.whl | CPython 3.13 | CPython 3.13 | Linux glibc 2.28+ x86-64 | Details |
| tmol-0.2.0-cp313-cp313-manylinux_2_28_aarch64.whl | CPython 3.13 | CPython 3.13 | Linux glibc 2.28+ ARM64 | Details |
| tmol-0.2.0-cp313-cp313-macosx_14_0_arm64.whl | CPython 3.13 | CPython 3.13 | macOS 14.0+ ARM64 | Details |
| tmol-0.2.0-cp312-cp312-manylinux_2_28_x86_64.whl | CPython 3.12 | CPython 3.12 | Linux glibc 2.28+ x86-64 | Details |
| tmol-0.2.0-cp312-cp312-manylinux_2_28_aarch64.whl | CPython 3.12 | CPython 3.12 | Linux glibc 2.28+ ARM64 | Details |
| tmol-0.2.0-cp312-cp312-macosx_14_0_arm64.whl | CPython 3.12 | CPython 3.12 | macOS 14.0+ ARM64 | Details |
| tmol-0.2.0-cp311-cp311-manylinux_2_28_x86_64.whl | CPython 3.11 | CPython 3.11 | Linux glibc 2.28+ x86-64 | Details |
| tmol-0.2.0-cp311-cp311-manylinux_2_28_aarch64.whl | CPython 3.11 | CPython 3.11 | Linux glibc 2.28+ ARM64 | Details |
| tmol-0.2.0-cp311-cp311-macosx_14_0_arm64.whl | CPython 3.11 | CPython 3.11 | macOS 14.0+ ARM64 | Details |
Total release size: 275.0 MB
Release files / tmol-0.2.0.tar.gz
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