Reference
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Git
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Git Large File Support (LFS)
git lfs install --skip-smudge
- Conda environment manager for Python
See: https://conda.io/projects/conda/en/latest/user-guide/install/index.html
- Clone Repository
git clone https://github.com/ncats/hcase
- Pull Large Files
cd hcase
git lfs pull (this may take some time)
conda env create -f code/environment.yml (this step needs to be done only once, if you don't already have the hcase conda environment)
conda activate hcase (this step is necessary everytime you want to run the HCASE method and/or the experiments)
cd code/
bash reproduce_HCASE_experiments.sh
- Step 1: Create HCASE space using ChEMBL scaffolds
Run workflow in HCASE_Embedding.ipynb .
- Step 2: Embed approved drug molecules from DrugBank into HCASE ChEMBL Scaffold space, and visualize the kNNs (k=5) of select chemotypes. Additionally, this is compared to the position of the same select chemotypes and 25 randomly selected molecules.
Run workflow in HCASE_Experiments_KNN_in_HCASE_Space.ipynb .
- Step 3: HCASE Embedding of Cherry-Picked Scaffolds and their 50+50 Immediate Neighbors Based on Scaffold-Key Ordering
Run workflow in HCASE_Experiments_Cherry_Picked_Scaffolds.ipynb .
- Step 4: Create the embedding of CANVASS molecules into HCASE ChEMBL space.
Run workflow in HCASE_Experiments_CANVASS_into_HCASE_ChEMBL.ipynb .
- Step 5: Create the embedding of approved drug and CANVASS molecules into HCASE NatProd space.
Run workflow in HCASE_Experiments_Drugs_and_CANVASS_into_HCASE_NatProd.ipynb .
- Step 6: Comparing HCASE ChEMBL and NatProd embeddings of approved drugs and CANVASS compounds
Run workflow in HCASE_Experiments_Compare_HCASE_ChEMBL_NatProd_Embeddings.ipynb .
- Step 7: Quantify overlap of approved drugs and CANVASS compounds embeddings in HCASE ChEMBL and NatProd spaces
Run workflow in HCASE_Experiments_Quantify_Overlap.ipynb .
- Step 8: Chebyshev Distance and Rank Distance Correlation.
Run workflow in HCASE_Experiments_Chebyshev.ipynb .
- Step 9: T-SNE Embedding of drugs and highlighting KNNs (k=5) of select molecules. Repeating the embedding and visualization on a reduced set (90%) of drugs.
Run workflow in HCASE_Experiments_tSNE.ipynb .
- Step 10: Creating an HCASE space using a reduced set (90%) of ChEMBL scaffolds, and the previously cherry-picked scaffolds.
Run workflow HCASE_Embedding_Reduced.ipynb .
- Step 11: Creating the HCASE embedding of the cherry-picked scaffolds into the reduced HCASE ChEMBL space generated in the previous step.
Run workflow HCASE_Experiments_Cherry_Picked_Scaffolds_in_Reduced_HCASE_Space.ipynb .
- Step 12: Repeating Step 2, only the KNN analysis of approved drug molecules is performed in a reduced HCASE ChEMBL scaffold space (90% subset of original ChEMBL scaffold used to create the HCASE space). So-called Not-NN neighbor analysis is not repeated, only the KNNs of the randomly selected 5 drug molecules.
Run workflow HCASE_Experiments_KNN_in_Reduced_HCASE_Space.ipynb .
- Step 13: Perform embedding of cherry-picked scaffolds using the Altered t-SNE method into ChEMBL scaffold space.
Run workflow HCASE_Experiments_Altered_tSNE_CherryPicked_Scaffolds.ipynb .
- Step 14: Repeating the previous step (Step 13) utilizing the union of the 90% subset of the reference scaffolds and the Cherry-Picked scaffolds.
Run workflow HCASE_Experiments_Altered_tSNE_Reduced_HCASE_Space_CherryPicked_Scaffolds.ipynb .
- Step 15: Embed approved drug molecules from DrugBank into Altered t-SNE ChEMBL Scaffold space, and visualize the kNNs (k=5) of select chemotypes.
Run workflow HCASE_Experiments_Altered_tSNE_KNN.ipynb .
- Step 16: Repeating Step 15, i.e. performing the Altered t-SNE embedding of the same 5 randomly selected drug molecules and their KNNs with the difference that a 90% reduced Altered t-SNE scaffold space is used for the embedding.
Run workflow HCASE_Experiments_Reduced_Altered_tSNE_KNN.ipynb .
- Step 17: Aggregate embeddings.
Run workflow HCASE_Experiments_Aggregate.ipynb .
This repository contains source code, Jupyter notebooks, data and results files which are organized into various subdirectories.
- Source code subdirectories:
code/ and aux_code
- Data and results subdirectory:
data/
- Jupyter Notebooks:
Jupyter Notebooks can be found in the code/ and data/execute_notebooks/ subdirectories (files of .ipynb extension).
- Workflows:
KNIME workflows can be found in workflows/ subdirectory.
- Source Code License of HCASE Repository
The applicable license to source code can be found under filename: code/LICENSE and aux_code/LICENSE (same license types, i.e. [https://opensource.org/licenses/MIT](MIT License)). This license is applicable to all files recursively in the source code subdirectories as defined above. The files code/NOTES and aux_code/NOTES list source code modules that were utilized and their respective licenses. These modules have their own licenses which might be different from the Source Code License of this repository, and they need to be respected accordingly.
- Data License of HCASE Repository
The applicable license to data and results can be found under: data/LICENSE and plots/LICENSE ([https://creativecommons.org/licenses/by/4.0/legalcode.txt](Creative Commons Attribution 4.0 International Public License CC-BY 4.0 International)). This license is applicable to all files recursively in the data and results subdirectory as defined above. The files listed in data/NOTES and plots/NOTES lists input files and resources utilized to perform the experiments and are considered as derivative work of those resources. These input files and resources have their own licenses which might be different from the Data License of this repository, and they need to be respected accordingly. In the same file we also list which results files can be considered as derivative works, and we also list the the respective ascendent data source(s).
- Jupyter Notebook License of HCASE Repository
Jupyter Notebboks are special in the sense that they are comprised of source code, but they can also contain embedded data and plot (graph) sections. This duality is resolved via dual-licensing as follows. The code sections of Jupter Notebooks fall under the same license as source codes, i.e. the MIT License code/LICENSEwhereas data and plot sections embedded into the Jupyter Notebooks fall under the same license as data and result files, i.e. Creative Commons Attribution 4.0 International Public License CC-BY 4.0 International License data/LICENSE. Remarks enclosed in the code/NOTES file are also valid for code section of the Jupyter Notebooks. Remarks enclosed in the data/NOTES and plots/NOTES files are also valid for the embedded data and plots of the Jupyter Notebook files.
- Workflow License of HCASE Repository
KNIME workflows utilized in this study are contained in the workflows/ directory, and are subject of the "KNIME License Terms and Conditions" see: workflows/LICENSE.
- Links to licenses
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MIT License: https://opensource.org/licenses/MIT
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Creative Commons Attribution-ShareAlike 3.0 Unported License: https://creativecommons.org/licenses/by-sa/3.0/
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Creative Commons Attribution 4.0 International Public License (CC-BY 4.0 International): https://creativecommons.org/licenses/by/4.0/legalcode.txt
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GPL-2 License: https://www.gnu.org/licenses/old-licenses/gpl-2.0.en.html
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GPL-3 License: https://www.gnu.org/licenses/gpl-3.0.en.html
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Apache License 2.0: https://www.apache.org/licenses/LICENSE-2.0
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3-Clause BSD license: https://opensource.org/licenses/BSD-3-Clause, https://github.com/scikit-learn/scikit-learn/blob/master/COPYING
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KNIME: KNIME License Terms and Conditions https://www.knime.com/downloads/full-license
Exporting Conda Environment for Reproduction
Critical packages and versions:
hilbertcurve: 1.0.3 rdkit: 2018.09.1.0 networkx: 2.4
Exporting the environment:
conda env export --from-history | grep -v "^prefix: " > code/environment.yml
Manually modifying package versions of networkx and rdkit, and environment name:
name: hcase-env
- rdkit==2018.09.1.0
- networkx==2.4
https://stackoverflow.com/questions/5226311/installing-specific-package-versions-with-pip?rq=1
https://stackoverflow.com/questions/38411942/anaconda-conda-install-a-specific-package-version
https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html
https://stackoverflow.com/questions/41274007/anaconda-export-environment-file
https://stackoverflow.com/questions/43222407/how-to-list-package-versions-available-with-conda
https://www.markdownguide.org/basic-syntax/
https://stackoverflow.com/questions/62852772/error-when-getting-git-lfs-files-object-does-not-exist-on-the-server-404-obj