/openDMPK

openDMPK, a tool to evaluate drug metabolism and Pharmacokinetic properties

Primary LanguagePythonMIT LicenseMIT

openDMPK

Prediction of ADMET properties for drug or durg like compounds

  • A total of 20 pharmokinetic propeties can be predicted using (18 classification and 2 regression models)
  • The classification models predict the probability of being active (eg. toxic), where as regression models predict a numeric quantification of the pharmacokinetic or toxicity property

Set up

    conda create -c conda-forge -n opendmpk rdkit==2018.09.1
    conda activate opendmpk
    pip install -r requirements.txt

Usage: python run_openDMPK.py [-h] [--smiles SMILES]

Example: python run_openDMPK.py --smiles "OC(O)C(Cl)(Cl)Cl"
Results:

         {'OC(O)C(Cl)(Cl)Cl': {'AmesMutagenesis': 0.0, 'AvianToxicity': 0.07, 
                        'BBBpenetration': 0.08, 'Biodegradation': 0.37, 
                        'CYP2c9': 0.14, 'CYP2d6': 0.09, 'CaCO2': 0.46, 
                        'EyeCorrosion': 0.27, 'EyeIrritation': 0.83, 
                        'HumanIntestineAbsorption': 0.97, 'HumanOralBioavailability': 0.47, 
                        'OrganicCationTransporter2': 0.16, 'hERGG': 0.68, 
                        'PlasmaProteinBinding': '0.3422 %', 
                        'TetrahymenaPyriformisToxicity': '0.1885 pIGC50 (ug/L)'}}

Docker

  • Build the docker image docker build -t opendmpk . and run docker run --rm opendmpk . Provide SMILES as docker run --rm opendmpk <compound_smiles>.
  • Download: docker pull kcgovinda/opendmpk:latest
  • Run the container: docker run --rm kcgovinda/opendmpk:latest --smiles <compound_smiles>.