molplotly

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 data points.

➡️  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 :)

⬇️ Installation

pip install molplotly
conda install rdkit

📜  Usage

import pandas as pd
import plotly.express 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,
                            df=df_esol,
                            smiles_col='smiles',
                            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 :P

Acknowledgements

• @wjm41 (contributor)
• @RokasEl (contributor)