scikit-mol/mkdocs.yml at main · EBjerrum/scikit-mol · GitHub

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site_name: "scikit-mol"
site_description: "scikit-learn classes for molecular vectorization using RDKit"
repo_url: "https://github.com/EBjerrum/scikit-mol"
repo_name: "EBjerrum/scikit-mol"
copyright: Copyright 2022 - 2025

use_directory_urls: true
docs_dir: "docs"

theme:
  name: material
  features:
    - navigation.tabs
    - navigation.expand

extra_javascript:
    - assets/js/readthedocs.js

extra_css:
  - assets/css/tweak-width.css

watch:
  - "scikit_mol"

plugins:
  - search
  - autorefs
  - mkdocstrings:
      handlers:
        python:
          options:
            docstring_style: numpy
          inventories:
            - https://scikit-learn.org/stable/objects.inv
            - https://docs.python.org/objects.inv
            - https://www.rdkit.org/docs/objects.inv
  - mkdocs-jupyter:
      execute: false
      include: ["*.ipynb"]

nav:
  - Overview: index.md
  - API:
    - scikit-mol.applicability: api/scikit_mol.applicability.md
    - scikit-mol.core: api/scikit_mol.core.md
    - scikit-mol.conversion: api/scikit_mol.conversions.md
    - scikit-mol.descriptors: api/scikit_mol.descriptors.md
    - scikit-mol.fingerprints: api/scikit_mol.fingerprints.md
    - scikit_mol.fingerprints.baseclasses: api/fingerprints.base.md
    - scikit-mol.parallel: api/scikit_mol.parallel.md
    - scikit-mol.plotting: api/scikit_mol.plotting.md
    - scikit-mol.safeinference: api/scikit_mol.safeinference.md
    - scikit-mol.standardizer: api/scikit_mol.standardizer.md

  - Notebooks:
    - Basic Usage and fingerprint transformers: notebooks/01_basic_usage.ipynb
    - Descriptor transformer: notebooks/02_descriptor_transformer.ipynb
    - Pipelining with Scikit-Learn classes: notebooks/03_example_pipeline.ipynb
    - Molecular standardization: notebooks/04_standardizer.ipynb
    - Sanitizing SMILES input: notebooks/05_smiles_sanitization.ipynb
    - Integrated hyperparameter tuning of Scikit-Learn estimator and Scikit-Mol transformer: notebooks/06_hyperparameter_tuning.ipynb
    - Using parallel execution to speed up descriptor and fingerprint calculations: notebooks/07_parallel_transforms.ipynb
    - Using skopt for hyperparameter tuning: notebooks/08_external_library_skopt.ipynb
    - Testing different fingerprints as part of the hyperparameter optimization: notebooks/09_Combinatorial_Method_Usage_with_FingerPrint_Transformers.ipynb
    - Using pandas output for easy feature importance analysis and combine pre-existing values with new computations: notebooks/10_pipeline_pandas_output.ipynb
    - Working with pipelines and estimators in safe inference mode: notebooks/11_safe_inference.ipynb
    - Creating custom fingerptint transformers: notebooks/12_custom_fingerprint_transformer.ipynb
    - Estimating applicability domain using feature based estimators: notebooks/13_applicability_domain.ipynb

  - Contributing: contributing.md