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molplotly is an add-on to plotly built on RDKit which allows 2D images of molecules to be shown in plotly figures when hovering over the datapoints.

Beautiful :)
Beautiful :)

Required packages:

➡️  A readable walkthrough of how to use the package together with some useful examples can be found in this blog post while a runnable notebook can be found in example.ipynb ?

?  Usage

import pandas as pd
import as px

import molplotly

# load a DataFrame with smiles
df_esol = pd.read_csv('esol.csv')
df_esol['y_pred'] = df_esol['ESOL predicted log solubility in mols per litre']
df_esol['y_true'] = df_esol['measured log solubility in mols per litre']

# generate a scatter plot
fig = px.scatter(df_esol, x="y_true", y="y_pred")

# add molecules to the plotly graph - returns a Dash app
app = molplotly.add_molecules(fig=fig, 
                            title_col='Compound ID', 

# run Dash app inline in notebook (or in an external server)
app.run_server(mode='inline', port=8011, height=1000)

Input parameters

  • fig : plotly.graph_objects.Figure object
    a plotly figure object containing datapoints plotted from df
  • df : pandas.DataFrame object
    a pandas dataframe that contains the data plotted in fig
  • smiles_col : str, optional
    name of the column in df containing the smiles plotted in fig (default ‘SMILES’)
  • show_img : bool, optional
    whether or not to generate the molecule image in the dash app (default True)
  • title_col : str, optional
    name of the column in df to be used as the title entry in the hover box (default None)
  • show_coords : bool, optional
    whether or not to show the coordinates of the data point in the hover box (default True)
  • caption_cols : list, optional
    list of column names in df to be included in the hover box (default None)
  • caption_transform : dict, optional
    Functions applied to specific items in all cells. The dict must follow a key: function structure where the key must correspond to one of the columns in subset or tooltip. (default {})
  • color_col : str, optional
    name of the column in df that is used to color the datapoints in df – necessary when there is discrete conditional coloring (default None)
  • wrap : bool, optional
    whether or not to wrap the title text to multiple lines if the length of the text is too long (default True)
  • wraplen : int, optional
    the threshold length of the title text before wrapping begins – adjust when changing the width of the hover box (default 20)
  • width : int, optional
    the width in pixels of the hover box (default 150)
  • fontfamily : str, optional
    the font family used in the hover box (default ‘Arial’)
  • fontsize : int, optional
    the font size used in the hover box – the font of the title line is fontsize+2 (default 12)

Output parameters

by default a JupyterDash app is returned which can be run inline in a jupyter notebook or deployed on a server via app.run_server()

  • The recommended height of the app is 50+(height of the plotly figure).
  • For the port of the app, make sure you don’t pick the same port as another molplotly plot otherwise the tooltips will clash with each other!

?   Can I run this in colab?

JupyterDash is supposed to have support for Google Colab but at some point that seems to have broken… Keep an eye on the raised issue here!

?   Can I save these plots?

moltplotly works using a Dash app which is non-trivial to export because server side javascript is needed in addition to HTML/CSS styling (as detailed here)

Until I find a way to get around that, the best alternative is exporting the plotly figure without molecules showing ? as detailed in this page. If you want to use it in a presentation I’d suggest keeping the figure open in a browser and changing windows to it during your talk!

?  Warning about memory size

Just adding a warning here that memory usage in a notebook can increase significanly when using plotly (not molplotly‘s fault!). If you notice your jupyter notebook slowing down, plotly itself is a likely culprit… In that case I’d consider either using plotly with static image rendering, or … use seaborn ?



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