Knime

Impact of chemical transformations on the hERG inhibition¶

The inhibition of the human ether-a-go-go (hERG) ion channel may cause QT interval prolongation, which eventually can result in torsades de pointes (TdP) and even death. Hence cardiotoxicity caused by the inhibition of hERG is a major liability within the drug development process.[1] To avoid such a severe adverse effect, it makes good sense to understand which chemical transformation can have a positive impact on the hERG inhibition.

hERG_Matched_Molecular_Pair_analysis is a Knime workflow capable of fetching hERG data from the ChEMBL database. A Matched Molecular Pair analysis is then performed to allow a bar chart data visualisation. The positive, negative or neutral impact percentage of each transformation can be visualize as a stacked bar chart.

[1] Journal of Cheminformatics volume 11, Article number: 9 (2019)

In [1]:

import pandas as pd
import knime

%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import matplotlib

matplotlib.style.use('ggplot')

Define your Knime executable path.

In [ ]:

knime.executable_path = "/opt/knime_4.1.1/knime"

Define your Knime workspace:

In [2]:

workspace = "/mnt/DATA/knime_workspace_production"

Now pick the Knime workflow

In [3]:

workflow = "hERG_MMP_analysis_with_ChEMBL_data_Czodrowski_AG_5_min_of_fame"
knime.Workflow(workflow_path=workflow,workspace_path=workspace)

Out[3]:

Run the Knime workflow

In [5]:

with knime.Workflow(workflow_path=workflow,workspace_path=workspace) as wf:
    wf.execute()

Get the data from Knime as a Pandas DF

In [6]:

df = wf.data_table_outputs[0]

Start from here if Knime is not installed on your computer.

In [17]:

#df = pd.read_csv ('./data/hERG_MMP_ChEMBL.csv')

Reorganise the DF for plotting

In [14]:

df = df.sort_values(by=['Positive_impact']).set_index('Transformation')

Plot the impact of each chemical transformation the hERG inhibition.

Applying a transformation with a high positive impact in % might have a high likelyhood of decreasing the hERG liability of a molecule for which such a transformation was applied thus reducing the cardiotoxicity of this molecule.

In [15]:

ax = df.plot.barh(figsize=(10, 250), stacked=True, title="Chemical transformation impact in %", fontsize=16)

Getting back "Transformation" as a Pandas column

In [10]:

df = df.reset_index(level='Transformation')

In [11]:

df.head(10)

Out[11]:

	Transformation	Negative_impact	Neutral_impact	Positive_impact
0	c1ccccn1>>c1ccc(F)cc1	90.00	10.0	0.00
1	CCC>>CCCC	100.00	0.0	0.00
2	C(=O)O>>C(=O)OC	100.00	0.0	0.00
3	F>>OC(F)(F)F	100.00	0.0	0.00
4	c1cccc(F)c1>>c1ccc(Cl)cc1	100.00	0.0	0.00
5	c1cccnc1>>c1ccccc1	93.75	0.0	6.25
6	c1ccccc1>>c1ccc(C(F)(F)F)cc1	93.75	0.0	6.25
7	C>>CCCC	92.86	0.0	7.14
8	CC(F)(F)F>>C(F)(F)F	90.91	0.0	9.09
9	c1ccc(F)cc1>>c1ccc(C)cc1	90.91	0.0	9.09

The table le below was obtained from the Knime workflow:

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