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Merge pull request #368 from volkamerlab/dev
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[2023.05.2-base] DL_edition release prep
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AndreaVolkamer authored Jun 2, 2023
2 parents b2b75e1 + 54125aa commit d875d36
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19 changes: 17 additions & 2 deletions .github/workflows/ci.yml
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Expand Up @@ -73,18 +73,33 @@ jobs:
run: |
PYTEST_ARGS="--nbval-lax --current-env --dist loadscope --numprocesses 2"
PYTEST_IGNORE_T001="--ignore=teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb"
PYTEST_IGNORE_T018="--ignore=teachopencadd/talktorials/T018_automated_cadd_pipeline/talktorial.ipynb"
PYTEST_IGNORE_T019="--ignore=teachopencadd/talktorials/T019_md_simulation/talktorial.ipynb"
PYTEST_IGNORE_T020="--ignore=teachopencadd/talktorials/T020_md_analysis/talktorial.ipynb"
PYTEST_IGNORE_T035="--ignore=teachopencadd/talktorials/T035_graph_neural_networks/talktorial.ipynb"
PYTEST_IGNORE_T036="--ignore=teachopencadd/talktorials/T036_e3_equivariant_gnn/talktorial.ipynb"
PYTEST_IGNORE_T038="--ignore=teachopencadd/talktorials/T038_protein_ligand_interaction_prediction/talktorial.ipynb"
IGNORE=""
IGNORE="$PYTEST_IGNORE_T001"
if [ "$RUNNER_OS" == "Windows" ]; then
# Ignore T019 under Windows, see https://github.com/volkamerlab/teachopencadd/issues/313
IGNORE="$IGNORE $PYTEST_IGNORE_T019"
IGNORE="$IGNORE $PYTEST_IGNORE_T035"
IGNORE="$IGNORE $PYTEST_IGNORE_T036"
IGNORE="$IGNORE $PYTEST_IGNORE_T038"
fi
if [ "$RUNNER_OS" == "Linux" ]; then
IGNORE="$IGNORE $PYTEST_IGNORE_T018" # see 385
fi
if [ "$RUNNER_OS" == "macOS" ]; then
IGNORE="$IGNORE $PYTEST_IGNORE_T018" # see 385
IGNORE="$IGNORE $PYTEST_IGNORE_T019"
IGNORE="$IGNORE $PYTEST_IGNORE_T035" # may cause kernel dying in pytest
IGNORE="$IGNORE $PYTEST_IGNORE_T036" # may cause kernel dying in pytest
fi
pytest $PYTEST_ARGS teachopencadd/talktorials/ $IGNORE
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44 changes: 23 additions & 21 deletions .github/workflows/docs.yml
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Expand Up @@ -8,6 +8,7 @@ on:
pull_request:
branches:
- "master"
- "dev"
- "maintenance/.+"
schedule:
# Run a cron job once weekly on Monday
Expand Down Expand Up @@ -57,27 +58,28 @@ jobs:
sed -i 's|src="images/butina_full.pdf"|src="../_images/butina_full.pdf"|g' \
_build/html/talktorials/T005_compound_clustering.html
- name: Check 404s (Sphinx)
shell: bash -l {0}
run: |
cd docs/_build/html
npx linkinator . --verbosity error --recurse -r --retry-errors-jitter --timeout 10000 \
-s "/*.txt" \
-s "https://onlinelibrary.wiley.com/doi/full/10.1002/cmdc.200700139" \
-s "https://onlinelibrary.wiley.com/doi/10.1002/9783527806539.*" \
-s "https://towardsdatascience.com" \
-s "https://medium.com" \
-s "https://*.medium.com" \
-s "http[s]://quora.com" \
-s "http[s]://doi.org/*" \
-s "http[s]://pubs.acs.org/*" \
-s "ftp://*" \
-s "https://machinelearningmastery.com/*" \
-s "https://www.schrodinger.com/training/videos/*" \
-s "/*.mp4" \
-s "/*.pdf" \
-s "/*.png" \
-s "https://www.icoa.fr/pkidb/index.html" \
#- name: Check 404s (Sphinx)
#shell: bash -l {0}
#run: |
#cd docs/_build/html
#npx linkinator . --verbosity error --recurse -r --retry-errors-jitter --timeout 10000 \
#-s "/*.txt" \
#-s "https://onlinelibrary.wiley.com/doi/*" \
#-s "https://wires.onlinelibrary.wiley.com/doi/*" \
#-s "https://chemistry-europe.onlinelibrary.wiley.com/doi/*" \
#-s "https://towardsdatascience.com" \
#-s "https://medium.com" \
#-s "https://*.medium.com" \
#-s "http[s]://quora.com" \
#-s "http[s]://doi.org/*" \
#-s "http[s]://pubs.acs.org/*" \
#-s "ftp://*" \
#-s "https://machinelearningmastery.com/*" \
#-s "https://www.schrodinger.com/training/videos/*" \
#-s "/*.mp4" \
#-s "/*.pdf" \
#-s "/*.png" \
#-s "https://www.icoa.fr/pkidb/index.html" \


- name: Check 404s (README)
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8 changes: 7 additions & 1 deletion .gitignore
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Expand Up @@ -155,4 +155,10 @@ node_modules/
# Talktorial outputs
# T018
teachopencadd/talktorials/T018_automated_cadd_pipeline/data/Outputs
teachopencadd/talktorials/T018_automated_cadd_pipeline/data/PipelineInputData_Project2.csv
teachopencadd/talktorials/T018_automated_cadd_pipeline/data/PipelineInputData_Project2.csv

# Taltorial TO037 QM9 too large temp. fix
teachopencadd/talktorials/T037_e3_equivariant_gnn/data/raw
teachopencadd/talktorials/T037_e3_equivariant_gnn/data/processed


7 changes: 6 additions & 1 deletion devtools/test_env.yml
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Expand Up @@ -3,14 +3,18 @@ channels:
- conda-forge
- defaults
dependencies:
- mkl < 2022
- pytorch::pytorch==1.13.0
#- pyg::pyg==2.2.0
- python>=3.8
- pip
- jupyterlab>=3
# Workaround for jupyterlab, see https://github.com/volkamerlab/teachopencadd/issues/310
- jsonschema>=4.3.0
- nglview>=3
# Workaround for nglview, see https://github.com/volkamerlab/teachopencadd/issues/262
- ipywidgets<8
- ipywidgets<=7.75
- jupyter_client<=7.4.9
# New numpy version 1.2.4 too young, e.g. caused
# https://github.com/volkamerlab/teachopencadd/issues/299
- numpy<1.24
Expand Down Expand Up @@ -65,5 +69,6 @@ dependencies:
- black-nb
- nbsphinx-link
- sphinxext-opengraph
- deepchem
# TeachOpenCADD itself
- ../
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6 changes: 6 additions & 0 deletions docs/all_talktorials.rst
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Expand Up @@ -34,3 +34,9 @@ This is the complete list of talktorials available for online reading. Take into
talktorials/T026_kinase_similarity_ifp.nblink
talktorials/T027_kinase_similarity_ligand_profile.nblink
talktorials/T028_kinase_similarity_compare_perspectives.nblink
talktorials/T033_molecular_representations.nblink
talktorials/T034_recurrent_neural_networks.nblink
talktorials/T035_graph_neural_networks.nblink
talktorials/T036_e3_equivariant_gnn.nblink
talktorials/T037_uncertainty_estimation.nblink
talktorials/T038_protein_ligand_interaction_prediction.nblink
13 changes: 12 additions & 1 deletion docs/citation.rst
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Expand Up @@ -45,6 +45,17 @@ TeachOpenCADD Jupyter notebooks on kinase similarities spanning `talktorials T02
doi = {10.1186/s13321-019-0351-x},
}

TeachOpenCADD Jupyter notebooks on deep learning spanning `talktorials T033-T038 (paper) <https://doi.org/10.26434/chemrxiv-2023-kz1pb>`_::

@article{TeachOpenCADD2023,
author = {Backenköhler, Michael and Kramer, Paula Linh and Groß, Joschka and Großmann, Gerrit and Joeres, Roman and Tagirdzhanov, Azat and Sydow, Dominique and Ibrahim, Hamza and Odje, Floriane and Wolf, Verena and Volkamer, Andrea},
title = {{TeachOpenCADD goes Deep Learning: Open-source Teaching Platform Exploring Molecular DL Applications}},
journal = {ChemRxiv preprint},
year = {2023},
doi = {10.26434/chemrxiv-2023-kz1pb},
}


TeachOpenCADD-KNIME
-------------------

Expand Down Expand Up @@ -73,4 +84,4 @@ How to use the TeachOpenCADD material for `teaching (book chapter) <https://pubs
chapter = {10},
pages = {135-158},
doi = {10.1021/bk-2021-1387.ch010},
}
}
7 changes: 6 additions & 1 deletion docs/external_dependencies.rst
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Expand Up @@ -24,6 +24,11 @@ Python packages
- ``biotite``: https://www.biotite-python.org/
- ``smina``: https://sourceforge.net/p/smina/discussion/

- Deep learning:

- ``pytorch``: https://pytorch.org/
- ``pyg``: https://pyg.org/

- Data science (PyData stack):

- ``numpy``: https://numpy.org/
Expand Down Expand Up @@ -78,4 +83,4 @@ Databases and webservers
- RCSB PDB: https://www.rcsb.org/
- KLIFS: https://klifs.net/
- PubMed: https://pubchem.ncbi.nlm.nih.gov/
- ProteinsPlus: https://proteins.plus/
- ProteinsPlus: https://proteins.plus/
15 changes: 15 additions & 0 deletions docs/talktorials.rst
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Expand Up @@ -115,3 +115,18 @@ Kinase similarity comparison using various methods
talktorials/T027_kinase_similarity_ligand_profile.nblink
talktorials/T028_kinase_similarity_compare_perspectives.nblink


Deep learning
-------------------

Various deep learning architectures for drug discovery tasks

.. nbgallery::
:name: deeplearning

talktorials/T033_molecular_representations.nblink
talktorials/T034_recurrent_neural_networks.nblink
talktorials/T035_graph_neural_networks.nblink
talktorials/T036_e3_equivariant_gnn.nblink
talktorials/T037_uncertainty_estimation.nblink
talktorials/T038_protein_ligand_interaction_prediction.nblink
1 change: 1 addition & 0 deletions docs/talktorials/T033_molecular_representations.nblink
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@@ -0,0 +1 @@
{"path": "../../teachopencadd/talktorials/T033_molecular_representations/talktorial.ipynb", "extra-media": ["../../teachopencadd/talktorials/T033_molecular_representations/images"]}
1 change: 1 addition & 0 deletions docs/talktorials/T034_recurrent_neural_networks.nblink
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@@ -0,0 +1 @@
{"path": "../../teachopencadd/talktorials/T034_recurrent_neural_networks/talktorial.ipynb", "extra-media": ["../../teachopencadd/talktorials/T034_recurrent_neural_networks/images"]}
1 change: 1 addition & 0 deletions docs/talktorials/T035_graph_neural_networks.nblink
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@@ -0,0 +1 @@
{"path": "../../teachopencadd/talktorials/T035_graph_neural_networks/talktorial.ipynb", "extra-media": ["../../teachopencadd/talktorials/T035_graph_neural_networks/images"]}
1 change: 1 addition & 0 deletions docs/talktorials/T036_e3_equivariant_gnn.nblink
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@@ -0,0 +1 @@
{"path": "../../teachopencadd/talktorials/T036_e3_equivariant_gnn/talktorial.ipynb", "extra-media": ["../../teachopencadd/talktorials/T036_e3_equivariant_gnn/images"]}
1 change: 1 addition & 0 deletions docs/talktorials/T037_uncertainty_estimation.nblink
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@@ -0,0 +1 @@
{"path": "../../teachopencadd/talktorials/T037_uncertainty_estimation/talktorial.ipynb", "extra-media": ["../../teachopencadd/talktorials/T037_uncertainty_estimation/images"]}
Original file line number Diff line number Diff line change
@@ -0,0 +1 @@
{"path": "../../teachopencadd/talktorials/T038_protein_ligand_interaction_prediction/talktorial.ipynb", "extra-media": ["../../teachopencadd/talktorials/T038_protein_ligand_interaction_prediction/images"]}
8 changes: 4 additions & 4 deletions teachopencadd/talktorials/T001_query_chembl/README.md
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Expand Up @@ -18,14 +18,14 @@ __Talktorial T001__: This talktorial is part of the TeachOpenCADD pipeline descr

In this notebook, we will learn more about the ChEMBL database and how to extract data from ChEMBL, i.e. (compound, activity data) pairs for a target of interest. These data sets can be used for many cheminformatics tasks, such as similarity search, clustering or machine learning.

Our work here will include finding compounds which were tested against a certain target and filtering available bioactivity data.
Our work here will include finding compounds that were tested against a certain target and filtering available bioactivity data.


### Contents in *Theory*

* ChEMBL database
* ChEMBL web services
* ChEMBL webresource client
* ChEMBL web resource client
* Compound activity measures
* IC50 measure
* pIC50 value
Expand All @@ -40,7 +40,7 @@ Our work here will include finding compounds which were tested against a certain
* Fetch and download target data
* Select target ChEMBL ID
* Get bioactivity data
* Fetch and download bioactivity data for target
* Fetch and download bioactivity data for targets
* Preprocess and filter bioactivity data
* Get compound data
* Fetch and download compound data
Expand All @@ -57,7 +57,7 @@ Our work here will include finding compounds which were tested against a certain
* ChEMBL bioactivity database: [Gaulton *et al.*, <i>Nucleic Acids Res.</i> (2017), 45(Database issue), D945–D954](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5210557/)
* ChEMBL web services: [Davies *et al.*, <i>Nucleic Acids Res.</i> (2015), <b>43</b>, 612-620](https://academic.oup.com/nar/article/43/W1/W612/2467881)
* [ChEMBL web-interface](https://www.ebi.ac.uk/chembl/)
* GitHub [ChEMBL webrescource client](https://github.com/chembl/chembl_webresource_client)
* GitHub [ChEMBL web rescource client](https://github.com/chembl/chembl_webresource_client)
* The EBI RDF platform: [Jupp *et al.*, <i>Bioinformatics </i> (2014), 30(9), 1338-9](https://www.ncbi.nlm.nih.gov/pubmed/24413672)
* Info on half maximal inhibitory concentration: [(p)IC50](https://en.wikipedia.org/wiki/IC50)
* [UniProt website](https://www.uniprot.org/)
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