A Jupyter Notebook to access structures and data from the master worksheet

This notebook demonstates how to get the structures and data from the master worksheet, then convert the SMILES to molecule objects that allow some simple manipulisations and visualisations. SMILES (Simplified Molecular Input Line Entry System) is a line notation (a typographical method using printable characters) for entering and representing molecules and reactions. https://www.daylight.com/dayhtml/doc/theory/theory.smiles.html

Getting the data

In [1]:
#First get data from Google doc
!wget -O example.tsv https://docs.google.com/spreadsheets/d/1fAxwae9W--0BLCLU1KIGdXcVGvxiLiO7VxjKoEO2XHE/export?format=tsv '--no-check-certificate'

#The data is downloaded to a file called example.tsv in the same folder as the notebook, in tab separated format

--2019-07-26 11:36:51--  https://docs.google.com/spreadsheets/d/1fAxwae9W--0BLCLU1KIGdXcVGvxiLiO7VxjKoEO2XHE/export?format=tsv
Resolving docs.google.com (docs.google.com)... 216.58.198.174
Connecting to docs.google.com (docs.google.com)|216.58.198.174|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: unspecified [text/tab-separated-values]
Saving to: ‘example.tsv’

example.tsv             [ <=>                ]   2.17K  --.-KB/s    in 0s      

2019-07-26 11:36:52 (17.7 MB/s) - ‘example.tsv’ saved [2221]

Import the required python modules and then import the example.tsv file into a Pandas dataframe called datafile

In [2]:
from rdkit.Chem import AllChem as Chem
from rdkit.Chem.Draw import IPythonConsole
from rdkit.Chem import PandasTools
from rdkit.Chem import Draw



import pandas as pd
#datafile = pd.read_table('./export?format=tsv')
datafile = pd.read_csv('example.tsv', sep = '\t')
In [3]:
#Allow inline images
%matplotlib inline
In [4]:
#View first five rows
datafile.head(5)
Out[4]:
OSA_ID Target SMILES_parent Name InChiKey PubChemCID VendorID MW_parent Fragalysis_ref Source Status Wiki link
0 OSA_000001 MurD CN1CCN(CC1)c1ccc(cc1)C#N 4-(4-methylpiperazin-1-yl)benzonitrile InChIKey=ZSDPKKGOSKXEHN-UHFFFAOYSA-N 763205 Z2856434840 201.273 MURD-x0349 FragLibrary In Library https://github.com/opensourceantibiotics/murli...
1 OSA_000002 MurD CN1CCN(CC1)C(=O)NC1=CC=C(F)C=C1 N-(4-fluorophenyl)-4-methylpiperazine-1-carbox... InChiKey=MDBPFVSVLGYVCQ-UHFFFAOYSA-N 851986 Z2856434944 237.280 MURD-x0373 FragLibrary In Library https://github.com/opensourceantibiotics/murli...
2 OSA_000003 MurD CC(CO)(CO)NC(=O)Nc1ccccc1 3-(1,3-dihydroxy-2-methylpropan-2-yl)-1-phenyl... InChiKey=NLGYHTMGWVQQIL-UHFFFAOYSA-N 4056614 Z57472297 224.260 MURD-x0374 FragLibrary In Library https://github.com/opensourceantibiotics/murli...
3 OSA_000004 MurD CC(C)C(=O)Nc1cccc(c1)C#N N-(3-cyanophenyl)-2-methylpropanamide InChIKey=JWBISRKEEZGPFB-UHFFFAOYSA-N 16383207 Z26548083 188.230 MURD-x0378 FragLibrary In Library https://github.com/opensourceantibiotics/murli...
4 OSA_000005 MurE O=S1(CCN(CC1)Cc2ccc(C)cc2)=O 4-(4-methylbenzyl)thiomorpholine 1,1-dioxide PBEMXBVPRZGFNM-UHFFFAOYSA-N 2815708 Z2856434929 239.330 MUREECA-x0198_3 FragLibrary In Library https://github.com/opensourceantibiotics/murli...
In [5]:
#Find how many rows
len(datafile.index)
Out[5]:
8

Convert the SMILES string to an RDKit molecular object

At the moment the molecule structures are represented by a SMILES string, we can convert the SMILES string to an RDKit molecular object and then display

In [6]:
#Remember the first row is row zero.
smiles = datafile['SMILES_parent'].loc[2]
In [7]:
#convert SMILES string to a RDKit molecular object
mol = Chem.MolFromSmiles(smiles)
In [8]:
mol
Out[8]:

We can see the different datatypes in the dataframe

In [9]:
datafile.dtypes
Out[9]:
OSA_ID             object
Target             object
SMILES_parent      object
Name               object
InChiKey           object
PubChemCID          int64
VendorID           object
MW_parent         float64
Fragalysis_ref     object
Source             object
Status             object
Wiki link          object
dtype: object

Adding structures to pandas dataframe

We can now convert the SMILES string to a RDKit molecular object for every row in the dataframe

In [10]:
PandasTools.AddMoleculeColumnToFrame(datafile,'SMILES_parent','Molecule',includeFingerprints=True)
>>> print([str(x) for x in  datafile.columns])

['OSA_ID', 'Target', 'SMILES_parent', 'Name', 'InChiKey', 'PubChemCID', 'VendorID', 'MW_parent', 'Fragalysis_ref', 'Source', 'Status', 'Wiki link', 'Molecule']
In [11]:
datafile.dtypes
Out[11]:
OSA_ID             object
Target             object
SMILES_parent      object
Name               object
InChiKey           object
PubChemCID          int64
VendorID           object
MW_parent         float64
Fragalysis_ref     object
Source             object
Status             object
Wiki link          object
Molecule           object
dtype: object

If we view the dataframe the molecule object has been added to the last column. It would be better if the structure was more readily visible. So we change the column order.

In [12]:
datafile.head(3)
Out[12]:
OSA_ID Target SMILES_parent Name InChiKey PubChemCID VendorID MW_parent Fragalysis_ref Source Status Wiki link Molecule
0 OSA_000001 MurD CN1CCN(CC1)c1ccc(cc1)C#N 4-(4-methylpiperazin-1-yl)benzonitrile InChIKey=ZSDPKKGOSKXEHN-UHFFFAOYSA-N 763205 Z2856434840 201.273 MURD-x0349 FragLibrary In Library https://github.com/opensourceantibiotics/murligase/wiki/MurD-fragment-screen Mol
1 OSA_000002 MurD CN1CCN(CC1)C(=O)NC1=CC=C(F)C=C1 N-(4-fluorophenyl)-4-methylpiperazine-1-carboxamide InChiKey=MDBPFVSVLGYVCQ-UHFFFAOYSA-N 851986 Z2856434944 237.280 MURD-x0373 FragLibrary In Library https://github.com/opensourceantibiotics/murligase/wiki/MurD-fragment-screen Mol
2 OSA_000003 MurD CC(CO)(CO)NC(=O)Nc1ccccc1 3-(1,3-dihydroxy-2-methylpropan-2-yl)-1-phenylurea InChiKey=NLGYHTMGWVQQIL-UHFFFAOYSA-N 4056614 Z57472297 224.260 MURD-x0374 FragLibrary In Library https://github.com/opensourceantibiotics/murligase/wiki/MurD-fragment-screen Mol
In [13]:
#display the current order
cols = list(datafile.columns.values)
cols
Out[13]:
['OSA_ID',
 'Target',
 'SMILES_parent',
 'Name',
 'InChiKey',
 'PubChemCID',
 'VendorID',
 'MW_parent',
 'Fragalysis_ref',
 'Source',
 'Status',
 'Wiki link',
 'Molecule']
In [14]:
#change the column order
datafile = datafile[['OSA_ID',
 'Molecule',
 'Target',
 'SMILES_parent',
 'Name',
 'InChiKey',
 'PubChemCID',
 'VendorID',
 'MW_parent',
 'Fragalysis_ref',
 'Source',
 'Status',
 'Wiki link']]
In [15]:
datafile.head(3)
Out[15]:
OSA_ID Molecule Target SMILES_parent Name InChiKey PubChemCID VendorID MW_parent Fragalysis_ref Source Status Wiki link
0 OSA_000001 Mol MurD CN1CCN(CC1)c1ccc(cc1)C#N 4-(4-methylpiperazin-1-yl)benzonitrile InChIKey=ZSDPKKGOSKXEHN-UHFFFAOYSA-N 763205 Z2856434840 201.273 MURD-x0349 FragLibrary In Library https://github.com/opensourceantibiotics/murligase/wiki/MurD-fragment-screen
1 OSA_000002 Mol MurD CN1CCN(CC1)C(=O)NC1=CC=C(F)C=C1 N-(4-fluorophenyl)-4-methylpiperazine-1-carboxamide InChiKey=MDBPFVSVLGYVCQ-UHFFFAOYSA-N 851986 Z2856434944 237.280 MURD-x0373 FragLibrary In Library https://github.com/opensourceantibiotics/murligase/wiki/MurD-fragment-screen
2 OSA_000003 Mol MurD CC(CO)(CO)NC(=O)Nc1ccccc1 3-(1,3-dihydroxy-2-methylpropan-2-yl)-1-phenylurea InChiKey=NLGYHTMGWVQQIL-UHFFFAOYSA-N 4056614 Z57472297 224.260 MURD-x0374 FragLibrary In Library https://github.com/opensourceantibiotics/murligase/wiki/MurD-fragment-screen

If we want to view all structures we can diaplay them as a grid

In [16]:
PandasTools.FrameToGridImage(datafile,column= 'Molecule', molsPerRow=4,subImgSize=(150,150),legendsCol="OSA_ID")
Out[16]:

Calculation of molecule properties

Now calculate a variety of properties using RDKit, adding them to the end of the dataframe. you can choose which properties to add here.

In [17]:
# Some of the availble descriptors are described here http://rdkit.org/docs/source/rdkit.Chem.rdMolDescriptors.html
from rdkit.Chem import rdMolDescriptors
In [18]:
hbdlist = [] #hydrogen bond donors
hbalist = [] #hydrogen bond acceptors
tpsalist = [] #Total polar surface area
mwtlist = [] #Exact molecular weight
logPlist = [] #Crippen LogP
mrlist = [] #Crippen MR
for mol in datafile['Molecule']:
    hbd = rdMolDescriptors.CalcNumHBD(mol)
    hbdlist.append(hbd)
    hba = rdMolDescriptors.CalcNumHBA(mol)
    hbalist.append(hba)
    TPSA = rdMolDescriptors.CalcTPSA(mol)
    tpsalist.append(TPSA)
    mwt = rdMolDescriptors.CalcExactMolWt(mol)
    mwtlist.append(mwt)
    crippen = rdMolDescriptors.CalcCrippenDescriptors(mol) #returns a 2-tuple with the Wildman-Crippen logp,mr values
    logPlist.append(crippen[0])#first is logP
    mrlist.append(crippen[1])#second is mr
In [19]:
#mrlist
In [ ]:

We now add each of the properties to the dataframe

In [20]:
datafile['HBD']=hbdlist
datafile['HBA']=hbalist
datafile['TPSA']=tpsalist
datafile['MWt']=mwtlist
datafile['LogP']=logPlist
datafile['MR']=mrlist
datafile.head(3)
Out[20]:
OSA_ID Molecule Target SMILES_parent Name InChiKey PubChemCID VendorID MW_parent Fragalysis_ref Source Status Wiki link HBD HBA TPSA MWt LogP MR
0 OSA_000001 Mol MurD CN1CCN(CC1)c1ccc(cc1)C#N 4-(4-methylpiperazin-1-yl)benzonitrile InChIKey=ZSDPKKGOSKXEHN-UHFFFAOYSA-N 763205 Z2856434840 201.273 MURD-x0349 FragLibrary In Library https://github.com/opensourceantibiotics/murligase/wiki/MurD-fragment-screen 0 3 30.27 201.126597 1.31008 60.8670
1 OSA_000002 Mol MurD CN1CCN(CC1)C(=O)NC1=CC=C(F)C=C1 N-(4-fluorophenyl)-4-methylpiperazine-1-carboxamide InChiKey=MDBPFVSVLGYVCQ-UHFFFAOYSA-N 851986 Z2856434944 237.280 MURD-x0373 FragLibrary In Library https://github.com/opensourceantibiotics/murligase/wiki/MurD-fragment-screen 1 2 35.58 237.127740 1.60500 64.4887
2 OSA_000003 Mol MurD CC(CO)(CO)NC(=O)Nc1ccccc1 3-(1,3-dihydroxy-2-methylpropan-2-yl)-1-phenylurea InChiKey=NLGYHTMGWVQQIL-UHFFFAOYSA-N 4056614 Z57472297 224.260 MURD-x0374 FragLibrary In Library https://github.com/opensourceantibiotics/murligase/wiki/MurD-fragment-screen 4 3 81.59 224.116092 0.55140 61.1730

We can also add a molecular properties

In [21]:
datafile['NumHeavyAtoms']=datafile.apply(lambda x: x['Molecule'].GetNumHeavyAtoms(), axis=1)
In [22]:
datafile.head(3)
Out[22]:
OSA_ID Molecule Target SMILES_parent Name InChiKey PubChemCID VendorID MW_parent Fragalysis_ref Source Status Wiki link HBD HBA TPSA MWt LogP MR NumHeavyAtoms
0 OSA_000001 Mol MurD CN1CCN(CC1)c1ccc(cc1)C#N 4-(4-methylpiperazin-1-yl)benzonitrile InChIKey=ZSDPKKGOSKXEHN-UHFFFAOYSA-N 763205 Z2856434840 201.273 MURD-x0349 FragLibrary In Library https://github.com/opensourceantibiotics/murligase/wiki/MurD-fragment-screen 0 3 30.27 201.126597 1.31008 60.8670 15
1 OSA_000002 Mol MurD CN1CCN(CC1)C(=O)NC1=CC=C(F)C=C1 N-(4-fluorophenyl)-4-methylpiperazine-1-carboxamide InChiKey=MDBPFVSVLGYVCQ-UHFFFAOYSA-N 851986 Z2856434944 237.280 MURD-x0373 FragLibrary In Library https://github.com/opensourceantibiotics/murligase/wiki/MurD-fragment-screen 1 2 35.58 237.127740 1.60500 64.4887 17
2 OSA_000003 Mol MurD CC(CO)(CO)NC(=O)Nc1ccccc1 3-(1,3-dihydroxy-2-methylpropan-2-yl)-1-phenylurea InChiKey=NLGYHTMGWVQQIL-UHFFFAOYSA-N 4056614 Z57472297 224.260 MURD-x0374 FragLibrary In Library https://github.com/opensourceantibiotics/murligase/wiki/MurD-fragment-screen 4 3 81.59 224.116092 0.55140 61.1730 16

Plotting properties

We can using seaborn (http://seaborn.pydata.org/index.html) a Python visualization library based on matplotlib to generate a variety of plots.

In [23]:
import seaborn as sns
In [24]:
myTPSA = datafile['TPSA']
myMWt = datafile['MWt']
In [25]:
#Scatter plot
sns.regplot(myMWt, myTPSA, fit_reg=False)
Out[25]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f926069e2b0>
In [26]:
#bar chart
sns.distplot(myMWt, kde=False, color='red', bins =10)
Out[26]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f92a03e26d8>
In [ ]:
In [ ]: