An Unsupervised Computational Pipeline Identifies Potential Repurposable Drugs to Treat Huntington’s Disease and Multiple Sclerosis
This is the official repository for the paper "An Unsupervised Computational Pipeline Identifies Potential Repurposable Drugs to Treat Huntington’s Disease and Multiple Sclerosis"
doi: 10.1016/j.ailsci.2022.100042
It contains the Python 3 implementation of the pipeline, as well as the results of its application on Huntington's disease and multiple sclerosis
Drug repurposing consists in identifying additional uses for known drugs and, since these new findings are built on previous knowledge, it reduces both the length and the costs of the drug development. In this work, we assembled an automated computational pipeline for drug repurposing, integrating also a network-based analysis for screening the possible drug combinations. The selection of drugs relies both on their proximity to the disease on the protein-protein interactome and on their influence on the expression of disease-related genes. Combined therapies are then prioritized on the basis of the drugs’ separation on the human interactome and the known drug-drug interactions. We eventually collected a number of molecules, and their plausible combinations, that could be proposed for the treatment of Huntington’s disease and multiple sclerosis. Furthermore, this pipeline could potentially provide new suggestions also for other complex disorders.
Keywords: Computational drug discovery, Drug repurposing, Network pharmacology, Network analysis, Huntington’s disease, Multiple sclerosis
Bibtex
@article {Menestrina2022,
author = {Menestrina, Luca and Recanatini, Maurizio},
doi = {10.1016/J.AILSCI.2022.100042},
issn = {2667-3185},
journal = {Artificial Intelligence in the Life Sciences},
pages = {100042},
publisher = {Elsevier},
title = {{An unsupervised computational pipeline identifies potential repurposable drugs to treat Huntington's disease and multiple sclerosis}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2667318522000125},
volume = {2},
year = {2022}
}
Download from GitHub:
wget https://github.com/LucaMenestrina/UnsupervisedComputationalFrameworkForDrugRepurposing/archive/refs/heads/master.zip
Install required packages:
pip install -r requirements.txt
Perform Analysis
python3 run.py [-h] [-d DISEASE] [-g GENES [GENES ...]] [-gf GENES_FILE] [-cl CELL_LINES [CELL_LINES ...]]
optional arguments:
-h, --help show this help message and exit
-d, --disease DISEASE Disease Name
-g, --genes GENES [GENES ...] Disease-Related Genes (separated by a space)
-gf, --genes_file GENES_FILE File Containing Disease-Related Genes (one per line)
-cl, --cell_lines CELL_LINES [CELL_LINES ...] Base Disease-Related Cell Lines in LINCS Database (separated by a space)
Specify only one of 'genes' and 'genes_file' If no Disease Name is provided the analysis will be performed for Huntington's disease and multiple sclerosis as in the paper
CAVEAT:
Some Sources (DrugBank, DisGeNET, OMIM) require identification for downloading their files.
Set the environmental variables: DRUGBANK_EMAIL, DRUGBANK_PASSWORD, DISGENET_EMAIL, DISGENET_PASSWORD, and OMIM_APIKEY or save them in a hidden file .env in the main directory of your project.
Additional info reported in sources.json