Package: drexml
Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transductionRepository for thedrexmlpython package: (DRExM³L) Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transductionSetupTo install thedrexmlpackage use the following:conda create -n drexml python=3.10
conda activate drexml
pip install drexmlIf a CUDA~10.2/11.x (< 12) compatible device is available use:conda create -n drexml --override-channels -c "nvidia/label/cuda-11.8.0" -c conda-forge cuda cuda-nvcc cuda-toolkit gxx=11.2 python=3.10
conda activate drexml
pip install --no-cache-dir --no-binary=shap drexmlTo installdrexmlin an existing environment, activate it and use:pip install drexmlNote that by default thesetupwill try to compile theCUDAmodules, if not possible it will use theCPUmodules.RunTo run the program for a disease map that uses circuits from the preprocessedKEGGpathways and theKDTstandard list, construct an environment file (e.g.disease.env):using the following template if you have a set of seed genes (comma-separated):seed_genes=2175,2176,2189using the following template if you want to use the DisGeNET [1] curated gene-disease associations as seeds.disease_id="C0015625"using the following template if you know which circuits to include (the disease map):circuits=circuits.tsv.gzTheTSVfilecircuits.tsvhas the following format (tab delimited):index	in_disease
P-hsa03320-37	0
P-hsa03320-61	0
P-hsa03320-46	0
P-hsa03320-57	0
P-hsa03320-64	0
P-hsa03320-47	0
P-hsa03320-65	0
P-hsa03320-55	0
P-hsa03320-56	0
P-hsa03320-33	0
P-hsa03320-58	0
P-hsa03320-59	0
P-hsa03320-63	0
P-hsa03320-44	0
P-hsa03320-36	0
P-hsa03320-30	0
P-hsa03320-28	1where:index: Hipathia circuit idin_disease: (boolean) True/1 if a given circuit is part of the diseaseNote that in all cases you can restrict the circuits to the physiological list by settinguse_physio=truein theenvfile.To run the experiment using 10 CPU cores and 0 GPUs, run the following command within an activated environment:drexml run --n-gpus 0 --n-cpus 10 $DISEASE_PATHwhere:--n-gpusindicates the number of gpu devices to use in parallel (-1 -> all) (0 -> None)--n-cpusindicates the number of cpu devices to use in parallel (-1 -> all) 8DISEASE_PATHindicates the path to the disease env file (e.g./path/to/disease/folder/disease.env)Use the--debugoption for testing that everything works using a few iterations.Note that the first time that the full program is run, it will take longer as it downloads the latest versions of each background dataset from Zenodo:https://doi.org/10.5281/zenodo.6020480Contribute to developmentThe recommended setup is:setuppipxsetupminiforgeusepipxto installpdmensure thatpdmis version >=2.1, otherwise update withpipxusepipxto inject pdm-bump intopdmusepipxto installnoxrunpdm config venv.backend condarunmake, if you want to use a CUDA enabled GPU, usemake gpu=1(Recommended): For GPU development, clear the cache usingpdm clean cachefirstDocumentationThe documentation can be found here:https://loucerac.github.io/drexml/References[1] Janet Piñero, Juan Manuel Ramírez-Anguita, Josep Saüch-Pitarch, Francesco Ronzano, Emilio Centeno, Ferran Sanz, Laura I Furlong. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucl. Acids Res. (2019) doi:10.1093/nar/gkz1021