Jupyter Notebook to access the IBM RXN API¶
This notebook can be used to to generate retrosynthetic routes to compounds selected from the docking studies¶
In [1]:
import sys
import time
from collections import defaultdict
from rdkit import Chem
from rdkit.Chem import AllChem
from rdkit.Chem.Draw import IPythonConsole
from rdkit.Chem import PandasTools
import pandas as pd
IPythonConsole.ipython_3d=True
%pylab inline
Get your api key¶
In [2]:
# you need to install rxn4chemistry using pip
# pip install rxn4chemistry
#You will need to register and get an api key from here https://rxn.res.ibm.com/rxn/user/profile
api_key='Enter your api key here'
from rxn4chemistry import RXN4ChemistryWrapper
rxn4chemistry_wrapper = RXN4ChemistryWrapper(api_key=api_key)
In [3]:
# rxn4chemistry_wrapper.set_project('PROJECT_ID')
response = rxn4chemistry_wrapper.create_project('my_covid19_candidates')
Choose the file contining your selected molecules¶
In [4]:
# File locations, this is the final output from your selection
SelectedsdfFilePath = 'selectedposeH.sdf'
In [5]:
selected_df = PandasTools.LoadSDF(SelectedsdfFilePath,molColName='Molecule', removeHs=True)
In [6]:
selected_df.head(5)
Out[6]:
| minimizedAffinity | name | RFScoreVS_v2 | ID | Molecule | |
|---|---|---|---|---|---|
| 0 | -5.03851 | SYN 13975176 | 6.312364 | SYN 13975176 |  |
| 1 | -3.77326 | ASN 10790639 | 6.305832 | ASN 10790639 |  |
| 2 | -5.91577 | AEM 10028511 | 6.30018 | AEM 10028511 |  |
| 3 | -5.96147 | SYN 13975163 | 6.298634 | SYN 13975163 |  |
| 4 | -5.11896 | LMG 16204894 | 6.294883 | LMG 16204894 |  |
In [7]:
selected_df['SMILES'] = selected_df.Molecule.apply(Chem.MolToSmiles)
In [8]:
selected_df.head(5)
Out[8]:
| minimizedAffinity | name | RFScoreVS_v2 | ID | Molecule | SMILES | |
|---|---|---|---|---|---|---|
| 0 | -5.03851 | SYN 13975176 | 6.312364 | SYN 13975176 | |
CN(C)CCN[C@@H]1CCN(Cc2ccccc2F)C[C@H]1O |
| 1 | -3.77326 | ASN 10790639 | 6.305832 | ASN 10790639 | |
COc1cc(CN2CCC(N)CC2)cc(OC)c1OC |
| 2 | -5.91577 | AEM 10028511 | 6.30018 | AEM 10028511 | |
Cc1cc(=O)[nH]c(NC2CCN(Cc3ccccc3)CC2)n1 |
| 3 | -5.96147 | SYN 13975163 | 6.298634 | SYN 13975163 | |
CN(C)CCN[C@@H]1CCN(Cc2ccc(F)cc2)C[C@H]1O |
| 4 | -5.11896 | LMG 16204894 | 6.294883 | LMG 16204894 | |
O=C1NCCCC12CCN(Cc1cccc(F)c1F)CC2 |
Choose the name of the molecule you want to make¶
In [9]:
ID = 'LMG 16204894'
theSMILES = selected_df.loc[selected_df['name'] == ID]['SMILES'].values[0]
theSMILES
Out[9]:
In [ ]:
Submit the SMILES for retrosynthesis¶
In [10]:
response = rxn4chemistry_wrapper.predict_automatic_retrosynthesis(product=theSMILES)
In [13]:
# rerun this until the status is 'SUCCESS', keep in mind the server allows only 5 requests per minute
# and a timeout between consecutive requests of 2 seconds
#If it times out, wait a couple of minutes and rerun.
import time
while True:
results = rxn4chemistry_wrapper.get_predict_automatic_retrosynthesis_results(response['prediction_id'])
if results['status'] == 'SUCCESS':
break
time.sleep(30) #check every 30 secs
In [14]:
def collect_reactions(tree):
reactions = []
if 'children' in tree and len(tree['children']):
reactions.append(
AllChem.ReactionFromSmarts('{}>>{}'.format(
'.'.join([node['smiles'] for node in tree['children']]),
tree['smiles']
), useSmiles=True)
)
for node in tree['children']:
reactions.extend(collect_reactions(node))
return reactions
Display the suggested routes¶
In [15]:
for index, path in enumerate(results['retrosynthetic_paths']):
print('Showing path {} with confidence {}:'.format(index, path['confidence']))
for reaction in collect_reactions(path):
display(Chem.Draw.ReactionToImage(reaction))
In [19]:
#to save images
for index, path in enumerate(results['retrosynthetic_paths']):
print('Showing path {} with confidence {}:'.format(index, path['confidence']))
for i, reaction in enumerate(collect_reactions(path)):
img = Chem.Draw.ReactionToImage(reaction)
img.save(f"{index}-{i}.png")
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