{
    "info": {
        "author": "Dingfeng Wu",
        "author_email": "dfw_bioinfo@126.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "# PisNRs\n\nPisNRs is a Python module for potential inhibitor and scaffold prediction \nof nuclear receptors, which constructed on top of RDKit, scikit-learn \nunder the MIT license.\n\n\n## Installation\n\nCurrently, PisNRs requires the following dependencies:\n\n- Python (>= 3.6)\n- rdkit (>= 2018.03.2.0)\n- scikit-learn (>= 0.19.1)\n- pandas (>= 0.23.1)\n\nAncaconda is recommended for package management and environment configures.\n\n### 1. Anaconda introduction\n\nThe user can download and install Anaconda at [Anaconda Python distribution](https://conda.io/docs/user-guide/install/index.html).\nAlso, Miniconda is acceptable in here. The conda source code repository is avaliable at\n [github](https://github.com/conda) and project [website](https://conda.io/docs/).\n\n### 2. Install RDKit with Anaconda\n\nCreating a new conda environment with the RDKit installation with the following command:\n\n~~~~~~~~~~~~~~~\n  $ conda create -c rdkit rdkit\n~~~~~~~~~~~~~~~\n\n### 3. Install scikit-learn\n\nUser can install scikit-learn by using ``pip`` :\n\n~~~~~~~~~~~~~~~\n    pip install -U scikit-learn\n~~~~~~~~~~~~~~~\n\nor ``conda`` :\n\n~~~~~~~~~~~~~~~\n    conda install scikit-learn\n~~~~~~~~~~~~~~~\n\n### 4. Install PisNRs\n\nAfter installation of RDKit and scikit-learn, PisNRs can be installed by using ``pip`` :\n\n~~~~~~~~~~~~~~~\n    pip install --upgrade pisnrs\n~~~~~~~~~~~~~~~\n\n## Example\n\n### 1. import PisNRs and load model\n\n~~~~~~~~~~~~~~~\n    import pisnrs\n    model = pisnrs.pisnrs()\n    print(model.getNRs()) #print NR proteins in model\n    print(model.getLigandDescriptors()) # print Ligand descriptors in model\n~~~~~~~~~~~~~~~\n\n### 2. Predict the activity and scaffold of query ligand\n\n~~~~~~~~~~~~~~~\n    ### moltype includes : smiles, mol, block, sdf\n    ### You can find the example .mol and .sdf file in the 'example/' folder\n    ### Example: https://github.com/ddhmed/pisnrs/tree/master/example\n    # 1. smiles input\n    smiles = 'CC1OC(C2=CC=CC=C2)=NC=1CN(CC1=CC(=C(C(=C1)C)OC(C(O)=O)(C)C)C)CC1OC=CC=1'\n    protein = 'NR1C1'\n    des = model.calPCMDecriptorFromMolText(smiles, protein, moltype='smiles') # create descriptors\n    print(model.preProba(des)) # predict\n\n    # 2. .mol file input\n    molfile = 'example/2.2_MolFile.mol'\n    protein = 'NR1C1'\n    des = model.calPCMDecriptorFromMolText(molfile, protein, moltype='mol')\n    print(model.preProba(des))\n\n    # 3. mol block input\n    block = '''\n     RDKit          2D\n\n 36 39  0  0  0  0  0  0  0  0999 V2000\n    4.9515   -5.4554    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n    4.4879   -6.8820    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n    5.3696   -8.0956    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n    4.4879   -9.3091    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n    4.9515  -10.7357    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n    6.4187  -11.0475    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n    6.8822  -12.4741    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n    5.8785  -13.5888    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n    4.4113  -13.2770    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n    3.9478  -11.8504    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n    3.0613   -8.8456    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n    3.0613   -7.3456    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n    1.8478   -6.4639    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n    0.4775   -7.0740    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n    0.3207   -8.5658    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -1.0496   -9.1759    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -2.2631   -8.2942    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -3.6335   -8.9043    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -3.7902  -10.3961    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -2.5767  -11.2778    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -1.2064  -10.6676    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -2.7335  -12.7695    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -5.1606  -11.0062    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n   -5.3174  -12.4980    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -5.4741  -13.9897    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -6.8445  -14.5999    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n   -4.2606  -14.8714    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n   -6.8091  -12.3412    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -3.8256  -12.6548    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -4.8470   -8.0226    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -0.7360   -6.1923    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -0.5792   -4.7005    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -1.6939   -3.6968    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n   -1.0838   -2.3265    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n    0.4079   -2.4833    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n    0.7198   -3.9505    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n  1  2  1  0\n  2  3  1  0\n  3  4  1  0\n  4  5  1  0\n  5  6  2  0\n  6  7  1  0\n  7  8  2  0\n  8  9  1  0\n  9 10  2  0\n  4 11  2  0\n 11 12  1  0\n 12 13  1  0\n 13 14  1  0\n 14 15  1  0\n 15 16  1  0\n 16 17  2  0\n 17 18  1  0\n 18 19  2  0\n 19 20  1  0\n 20 21  2  0\n 20 22  1  0\n 19 23  1  0\n 23 24  1  0\n 24 25  1  0\n 25 26  1  0\n 25 27  2  0\n 24 28  1  0\n 24 29  1  0\n 18 30  1  0\n 14 31  1  0\n 31 32  1  0\n 32 33  1  0\n 33 34  1  0\n 34 35  2  0\n 35 36  1  0\n 12  2  2  0\n 21 16  1  0\n 36 32  2  0\n 10  5  1  0\nM  END\n    '''\n    des = model.calPCMDecriptorFromMolText(block, protein, moltype='block')\n    print(model.preProba(des))\n\n    # 4. .sdf file input\n    sdffile = 'example/2.4_SDFFile.sdf'\n    protein = 'NR1C1'\n    des = model.calPCMDecriptorFromMolText(sdffile, protein, moltype='sdf')\n    print(model.preProba(des))\n~~~~~~~~~~~~~~~\n\n### 3. Derive molecule scaffold of query ligand\n\n~~~~~~~~~~~~~~~\n    smiles = 'CC1OC(C2=CC=CC=C2)=NC=1CN(CC1=CC(=C(C(=C1)C)OC(C(O)=O)(C)C)C)CC1OC=CC=1'\n    scaffold = model.calScaffoldFromSmiles(smiles)\n    print(scaffold)\n~~~~~~~~~~~~~~~\n\n### 4. Create molucule image of query ligand\n\n~~~~~~~~~~~~~~~\n    smiles = 'CC1OC(C2=CC=CC=C2)=NC=1CN(CC1=CC(=C(C(=C1)C)OC(C(O)=O)(C)C)C)CC1OC=CC=1'\n    model.image_from_smiles(smiles, name='4_OutputImage.png', dir='example/') # output image of smiles to 'example/' folder\n~~~~~~~~~~~~~~~\n\n### 5. Batch mode uploading\n\n~~~~~~~~~~~~~~~\n    smiles_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']\n    protein_list = ['NR1C1', 'NR1C2']\n    print(model.preBatch(smiles_list, protein_list)) # predict activity of every ligands and proteins in list \n    print(model.preBatch(smiles_list)) # predict activity of every ligands in list and all proteins in model\n    ### load smiles list from file\n    smiles_file = 'example/5_SmilesList.smiles'\n    smiles_list = [i.strip() for i in open(smiles_file, 'r').readlines()]\n    print(model.preBatch(smiles_list))\n~~~~~~~~~~~~~~~\n\n## Related links\n\n- Official source code repo: https://github.com/ddhmed/pisnrs\n- Download releases: https://pypi.org/project/pisnrs/\n- web server : http://pisnrs.com/predict\n\n## Source code\n\nThe latest sources can be checked by using the following command:\n\n    git clone https://github.com/ddhmed/pisnrs.git\n\n\n",
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