Plotting Molecule Data with 2D Depictions

Molecule Plot Examples

by Matt Geballe

Many times in computational chemistry we plot molecules as individual data points, often in a scatter plot. Having images of the molecule (2D depictions of the molecular graph) as tooltips for the points on the plot is a very useful pattern so questions about interesting points can be pursued.

This notebook will cover attempts to construct these plots using different python plotting libraries.

Loading Data

We'll use OENotebook to read some molecules into a pandas DataFrame, then calculate some properties using the OEChem and MolProp tooltkits.

In [1]:
from __future__ import print_function

# import OE tools and data
import oenotebook as oenb
# oenb.capture_errors()

import warnings

from openeye.oechem import OECalculateMolecularWeight
from openeye.oemolprop import OEGetXLogP, OEGet2dPSA
%matplotlib inline

In [2]:
# Read molecules into a Pandas Dataframe
df = oenb.read_file_to_dataframe("./eMol_ran50.ism", title_col="Title")

# Calculate some Properties
df["MW"] = df.Molecule.apply(OECalculateMolecularWeight)
df["XlogP"] = df.Molecule.apply(OEGetXLogP)
# df["2D_PSA"] = df.Molecule.apply(OEGet2dPSA)

# Check to make sure data looks right
MW XlogP
count 50.000000 50.000000
mean 364.959820 3.117720
std 82.409009 1.444342
min 155.220760 -0.931000
25% 316.245407 2.189250
50% 360.088080 3.230000
75% 411.317835 3.989500
max 590.795900 6.190000

Matplotlib and mpld3

Matplotlib is one of the oldest, most widely-used, and fully-featured python plotting libraries. We will use it first to make a basic scatter plot, then use mpld3 to combine the matplotlib figure with D3js and generate an interactive visualization.

In [3]:
# import matplotlib and setup style
%matplotlib inline

from matplotlib import pyplot as plt'fivethirtyeight')

Simple scatter plot to compare Molecular Weight and XlogP

In [4]:
# Define figure

# Make the scatterplot

# Add axis labels and title

# plt.savefig("my_plot.png")

Add 2D depiction toolkits using MPLD3

In [5]:
import mpld3
In [6]:
# Create list of image URIs
molImgs = list(df.Molecule.apply(lambda x: oenb.draw_mol_to_img_tag(x,300,200)))
In [7]:
# Confirm our image creation worked
from IPython.display import HTML
In [8]:
Molecule Title MW XlogP
0 <oechem.OEMol; proxy of <Swig Object of type '... 25864246 338.529460 6.190000
1 <oechem.OEMol; proxy of <Swig Object of type '... 25167497 425.310520 4.533000
2 <oechem.OEMol; proxy of <Swig Object of type '... 24665960 289.716980 2.575000
3 <oechem.OEMol; proxy of <Swig Object of type '... 2251284 376.519520 4.237999
4 <oechem.OEMol; proxy of <Swig Object of type '... 5632287 419.421826 1.437000
In [9]:
import pandas as pd

# Define figure and axes, hold on to object since they're needed by mpld3
fig, ax = plt.subplots(figsize=(12,8))

# Make scatterplot and label axes, title
sc = ax.scatter(df.MW,df.XlogP,s=200,alpha=0.5,edgecolors='none')

# Create the mpld3 HTML tooltip plugin
tooltip = mpld3.plugins.PointHTMLTooltip(sc, molImgs)
# tooltip = mpld3.plugins.PointHTMLTooltip(sc, list(df.apply(lambda x: pd.DataFrame({x["Molecule"].GetTitle():[x["MW"],x["XlogP"]]},index=["MW","XlogP"]).to_html(escape=False))))

# Connect the plugin to the matplotlib figure
mpld3.plugins.connect(fig, tooltip)


# Uncomment to save figure to html file

This functionality is also built in to OENotebook.

In [10]:
f,a = oenb.scatter_mpl(df,"MW","XlogP")
In [11]:
f,a = oenb.scatter_mpl(df,"MW","XlogP",show_2D=False)


Bokeh is a plotting library designed from the beginning for interactive plots in modern browsers. It contains all the javascript components necessary to provide interactive tooltips.

In [13]:
from bokeh.plotting import figure, show, output_file, ColumnDataSource
from import output_notebook

from bokeh.models import HoverTool, Callback
from collections import OrderedDict

# Configure default tools for plots
TOOLS = 'box_zoom,box_select,resize,reset,save'

# Configure for output in the notebook
Loading BokehJS ...

Simple scatter plot with data tooltips

In [14]:
# Store data in a bokeh object data object
source = ColumnDataSource(df.drop("Molecule",axis=1))

p = figure(title="Molecules", tools = TOOLS)
p.xaxis.axis_label = 'MW'
p.yaxis.axis_label = 'XLogP'

# Make scatterplot"MW", "XlogP", fill_alpha=0.2, size=10, source=source)

# Configure hover tooltip with simple information about each point
hover = HoverTool()
hover.tooltips = OrderedDict([
    ('Title', '@Title'),
    ('MW', '@MW'),
    ('XlogP', '@XlogP'),


<Bokeh Notebook handle for In[14] >

Now to add the images as tooltips

In [15]:
# I think HTML rendering of tooltips will be fixed in 0.9.3
# see

# Store data in a bokeh object data object and add images
source = ColumnDataSource(df.drop("Molecule",axis=1))
source.add(df.Molecule.apply(lambda x: oenb.draw_mol_to_html(x,300,200)),name="img")

p = figure(title="Molecule", tools=TOOLS)
p.xaxis.axis_label = 'MW'
p.yaxis.axis_label = 'XLogP'

# Make scatterplot"MW", "XlogP", fill_alpha=0.2, size=10, source=source)

# Create tooltips referencing stored images
tooltips = """<img src=@img>"""

# Connect tooltips to plot

# Uncomment to create a html file of the plot.
# output_file("bokeh_plot.html")


<Bokeh Notebook handle for In[15] >

Again, this functionality is built in to OENotebook.

In [16]:
f = oenb.scatter_bokeh(df,"MW","XlogP")
Loading BokehJS ...

<Bokeh Notebook handle for In[16] >

In [17]:
f = oenb.scatter_bokeh(df,"MW","XlogP",show_2D=False)
Loading BokehJS ...

<Bokeh Notebook handle for In[17] >


Toyplot is a new and simple plotting toolkit. Currently you can't make hover tooltips, but it's worth keeping an eye on it.

In [19]:
import toyplot
In [20]:
canvas = toyplot.Canvas(width=600, height=400)
axes = canvas.axes(label="Molecules", xlabel="MW", ylabel="XlogP")
mark = axes.scatterplot(df.MW,df.XlogP,size=100, opacity=0.5)
200 300 400 500 600 MW 0 2 4 6 XlogP Molecules
In [ ]:
In [21]:
# Version Reporting

from openeye.oechem import OEChemGetVersion
print("OEChem: {}".format(OEChemGetVersion()))
print("OENotebook: {}".format(oenb.__version__))
import matplotlib as mpl
print("matplotlib: {}".format(mpl.__version__))
print("mpld3: {}".format(mpld3.__version__))
import bokeh
print("bokeh: {}".format(bokeh.__version__))
print("toyplot: {}".format(toyplot.__version__))
OEChem: 20160209
OENotebook: 0.8.1

matplotlib: 1.5.1
mpld3: 0.2

bokeh: 0.11.1

toyplot: 0.11.0