SLAE/pyproject.toml at main · ProteinDesignLab/SLAE · GitHub

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[build-system]
requires = ["setuptools>=64", "wheel"]
build-backend = "setuptools.build_meta"

[project]
name = "SLAE"
version = "0.1.0"
description = "Strictly Local All-atom Environment for Protein Representation"
readme = "README.md"
requires-python = ">=3.10"
license = { text = "Apache-2.0" }
authors = [
    { name = "Yilin Chen", email = "yilinc5@stanford.edu" },
]
keywords = [
    "protein structure",
    "representation learning",
    "graph neural network",
    "all-atom",
    "autoencoder",
]
classifiers = [
    "License :: OSI Approved :: Apache Software License",
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.10",
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Topic :: Scientific/Engineering :: Bio-Informatics",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
]

# Runtime dependencies for autoencoder loading + inference.
#
# NOTE: `torch` and the PyTorch Geometric companion wheels
# (`torch-scatter`, `torch-cluster`, `torch-sparse`, `torch-runstats`) are
# compiled against a specific torch + CUDA build. On most systems you should
# install torch first and then the companion wheels from the matching PyG wheel
# index BEFORE `pip install SLAE` (see the README "Installation" section).
# They are listed here so the dependency specification is complete.
dependencies = [
    "torch>=2.2",
    # Pinned: torch-geometric 2.8 routes `radius_graph` through pyg-lib, whereas
    # the featurizer relies on the torch_cluster-backed implementation in 2.7.
    "torch-geometric==2.7.0",
    "torch-scatter",
    "torch-cluster",
    "torch-sparse",
    "torch-runstats",
    "e3nn>=0.5",
    "einops",
    "einx",
    "opt-einsum-fx",
    "esm>=3.0",
    "hydra-core>=1.3",
    "omegaconf>=2.3",
    "lightning>=2.2",
    "numpy<2",
    "biotite",
    "biopandas",
    "atomworks",
    "pandas",
    "loguru",
    "tqdm",
    "pyyaml",
    "packaging",
]

[project.optional-dependencies]
viz = ["py3dmol"]
dev = ["pytest>=7", "ruff>=0.4"]

[project.urls]
Homepage = "https://github.com/possu-lab/SLAE"
Paper = "https://www.biorxiv.org/content/10.1101/2025.10.03.680398v1"

[tool.setuptools.packages.find]
include = ["SLAE*"]

[tool.setuptools.package-data]
SLAE = ["configs/**/*.yaml"]

[tool.ruff]
line-length = 100
target-version = "py310"

[tool.ruff.lint]
# Keep the public release lint-clean without being noisy about research-code
# idioms (unused star-imports in nn building blocks, etc.).
select = ["E", "F", "W", "I"]
ignore = ["E501", "E741", "F403", "F405"]

[tool.ruff.lint.per-file-ignores]
# Inherited research internals are kept functionally byte-identical for this
# release (only whitespace/import-order are auto-formatted). We therefore do not
# enforce lint rules on them that would require logic edits -- e.g. a
# load-bearing duplicate key in `RES_TYPE_MAP` the pretrained model relies on,
# unused transform-registry imports, and an alternate decode path not used by
# the default config.
"SLAE/nn/**" = ["F401", "F811", "F841"]
"SLAE/model/**" = ["F401", "F811", "F821", "F841", "E712"]
"SLAE/features/**" = ["F401"]
"SLAE/io/**" = ["F401"]
"SLAE/datasets/**" = ["F401"]
"SLAE/util/**" = ["F401", "F811", "F841", "F601"]

[tool.pytest.ini_options]
testpaths = ["tests"]
addopts = "-q"