PisNRs 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.
Currently, 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.
The user can download and install Anaconda at Anaconda Python distribution. Also, Miniconda is acceptable in here. The conda source code repository is avaliable at github and project website.
Creating a new conda environment with the RDKit installation with the following command:
$ conda create -c rdkit rdkit
User can install scikit-learn by using pip :
pip install -U scikit-learn
or conda :
conda install scikit-learn
After installation of RDKit and scikit-learn, PisNRs can be installed by using pip :
pip install --upgrade pisnrs
import pisnrs
model = pisnrs.pisnrs()
print(model.getNRs()) #print NR proteins in model
print(model.getLigandDescriptors()) # print Ligand descriptors in model
### 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))
smiles = '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)
smiles = '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/' folder
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']
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))
- Official source code repo: https://github.com/ddhmed/pisnrs
- Download releases: https://pypi.org/project/pisnrs/
- web server : http://pisnrs.badd-cao.net/predict
The latest sources can be checked by using the following command:
git clone https://github.com/ddhmed/pisnrs.git