Package: pisnrs
PisNRsPisNRs is a Python module for potential inhibitor and scaffold prediction
of nuclear receptors, which constructed on top of RDKit, scikit-learn
under the MIT license.InstallationCurrently, PisNRs requires the following dependencies:Python (>= 3.6)rdkit (>= 2018.03.2.0)scikit-learn (>= 0.19.1)pandas (>= 0.23.1)Ancaconda is recommended for package management and environment configures.1. Anaconda introductionThe user can download and install Anaconda atAnaconda Python distribution.
Also, Miniconda is acceptable in here. The conda source code repository is avaliable atgithuband projectwebsite.2. Install RDKit with AnacondaCreating a new conda environment with the RDKit installation with the following command:$ conda create -c rdkit rdkit3. Install scikit-learnUser can install scikit-learn by usingpip:pip install -U scikit-learnorconda:conda install scikit-learn4. Install PisNRsAfter installation of RDKit and scikit-learn, PisNRs can be installed by usingpip:pip install --upgrade pisnrsExample1. import PisNRs and load modelimport pisnrs
    model = pisnrs.pisnrs()
    print(model.getNRs()) #print NR proteins in model
    print(model.getLigandDescriptors()) # print Ligand descriptors in model2. Predict the activity and scaffold of query ligand### moltype includes : smiles, mol, block, sdf
    ### You can find the example .mol and .sdf file in the 'example/' folder
    ### Example: https://github.com/ddhmed/pisnrs/tree/master/example
    # 1. smiles input
    smiles = 'CC1OC(C2=CC=CC=C2)=NC=1CN(CC1=CC(=C(C(=C1)C)OC(C(O)=O)(C)C)C)CC1OC=CC=1'
    protein = 'NR1C1'
    des = model.calPCMDecriptorFromMolText(smiles, protein, moltype='smiles') # create descriptors
    print(model.preProba(des)) # predict

    # 2. .mol file input
    molfile = 'example/2.2_MolFile.mol'
    protein = 'NR1C1'
    des = model.calPCMDecriptorFromMolText(molfile, protein, moltype='mol')
    print(model.preProba(des))

    # 3. mol block input
    block = '''
     RDKit          2D

 36 39  0  0  0  0  0  0  0  0999 V2000
    4.9515   -5.4554    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    4.4879   -6.8820    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    5.3696   -8.0956    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0
    4.4879   -9.3091    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    4.9515  -10.7357    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    6.4187  -11.0475    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    6.8822  -12.4741    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    5.8785  -13.5888    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    4.4113  -13.2770    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    3.9478  -11.8504    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    3.0613   -8.8456    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0
    3.0613   -7.3456    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    1.8478   -6.4639    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    0.4775   -7.0740    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0
    0.3207   -8.5658    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -1.0496   -9.1759    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -2.2631   -8.2942    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -3.6335   -8.9043    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -3.7902  -10.3961    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -2.5767  -11.2778    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -1.2064  -10.6676    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -2.7335  -12.7695    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -5.1606  -11.0062    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0
   -5.3174  -12.4980    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -5.4741  -13.9897    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -6.8445  -14.5999    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0
   -4.2606  -14.8714    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0
   -6.8091  -12.3412    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -3.8256  -12.6548    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -4.8470   -8.0226    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -0.7360   -6.1923    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -0.5792   -4.7005    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
   -1.6939   -3.6968    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0
   -1.0838   -2.3265    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    0.4079   -2.4833    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
    0.7198   -3.9505    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0
  1  2  1  0
  2  3  1  0
  3  4  1  0
  4  5  1  0
  5  6  2  0
  6  7  1  0
  7  8  2  0
  8  9  1  0
  9 10  2  0
  4 11  2  0
 11 12  1  0
 12 13  1  0
 13 14  1  0
 14 15  1  0
 15 16  1  0
 16 17  2  0
 17 18  1  0
 18 19  2  0
 19 20  1  0
 20 21  2  0
 20 22  1  0
 19 23  1  0
 23 24  1  0
 24 25  1  0
 25 26  1  0
 25 27  2  0
 24 28  1  0
 24 29  1  0
 18 30  1  0
 14 31  1  0
 31 32  1  0
 32 33  1  0
 33 34  1  0
 34 35  2  0
 35 36  1  0
 12  2  2  0
 21 16  1  0
 36 32  2  0
 10  5  1  0
M  END
    '''
    des = model.calPCMDecriptorFromMolText(block, protein, moltype='block')
    print(model.preProba(des))

    # 4. .sdf file input
    sdffile = 'example/2.4_SDFFile.sdf'
    protein = 'NR1C1'
    des = model.calPCMDecriptorFromMolText(sdffile, protein, moltype='sdf')
    print(model.preProba(des))3. Derive molecule scaffold of query ligandsmiles = 'CC1OC(C2=CC=CC=C2)=NC=1CN(CC1=CC(=C(C(=C1)C)OC(C(O)=O)(C)C)C)CC1OC=CC=1'
    scaffold = model.calScaffoldFromSmiles(smiles)
    print(scaffold)4. Create molucule image of query ligandsmiles = 'CC1OC(C2=CC=CC=C2)=NC=1CN(CC1=CC(=C(C(=C1)C)OC(C(O)=O)(C)C)C)CC1OC=CC=1'
    model.image_from_smiles(smiles, name='4_OutputImage.png', dir='example/') # output image of smiles to 'example/' folder5. Batch mode uploadingsmiles_list = ['CC1OC(C2=CC=CC=C2)=NC=1CN(CC1=CC(=C(C(=C1)C)OC(C(O)=O)(C)C)C)CC1OC=CC=1', 'C1=CC=CC=C1']
    protein_list = ['NR1C1', 'NR1C2']
    print(model.preBatch(smiles_list, protein_list)) # predict activity of every ligands and proteins in list 
    print(model.preBatch(smiles_list)) # predict activity of every ligands in list and all proteins in model
    ### load smiles list from file
    smiles_file = 'example/5_SmilesList.smiles'
    smiles_list = [i.strip() for i in open(smiles_file, 'r').readlines()]
    print(model.preBatch(smiles_list))Related linksOfficial source code repo:https://github.com/ddhmed/pisnrsDownload releases:https://pypi.org/project/pisnrs/web server :http://pisnrs.com/predictSource codeThe latest sources can be checked by using the following command:git clone https://github.com/ddhmed/pisnrs.git