# Cell 1. Install CondaColab: Enables Conda environment support in Google Colab

from IPython.utils import io
import tqdm.notebook
import os
 
total = 100
with tqdm.notebook.tqdm(total=total) as pbar:
   with io.capture_output() as captured:
 
       # Install CondaColab
       !pip install -q condacolab
       import condacolab
       condacolab.install()
       pbar.update(10)
 
       # Update Python path to locate installed packages
       import sys
       sys.path.append('/usr/local/lib/python3.7/site-packages/')
       pbar.update(20)
 
       # Install PyMOL bundle using mamba from the Schrodinger channel
       %shell mamba install -c schrodinger pymol-bundle --yes
       pbar.update(90)

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# Cell 2. Install ConPhar pharmacophore analysis package
 
import pymol
!pip install conphar
from conphar.Pharmacophores import parse_json_pharmacophore, show_pharmacophoric_descriptors, save_pharmacophore_to_pymol, save_pharmacophore_to_json, compute_concensus_pharmacophore
import os
import pandas as pd

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# Cell 3. Create a folder to store input JSON files

os.makedirs("JSON_FOLDER", exist_ok=True)

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#  Cell 4. 
 
p4_table=pd.DataFrame()
for file in os.listdir('/content/JSON_FOLDER'):
 if '.json' in file:
   try:
     p4,lig,rec=parse_json_pharmacophore(f"/content/JSON_FOLDER/{file}")
     p4['ligand']=file.replace('.json','')
     p4_table=pd.concat([p4_table,p4],ignore_index=True)
   except Exception:
     pass
p4_table

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#  Cell 5.

show_pharmacophoric_descriptors(p4_table)

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#  Cell 6.
 
save_pharmacophore_to_pymol(p4_table, out_file='ConPhar_pymol.pse')

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#  Cell 7.
 
save_pharmacophore_to_json(p4_table,out_file='ConPhar_pharmit.json')

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#  Cell 8.
 
concensus,links=compute_concensus_pharmacophore(p4_table,save_data_per_descriptor=True,out_folder='/content')

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#  Cell 9.
 
concensus.to_csv('consensus_result.csv', index=False)