Tag Archives: Jupyter

Interesting Jupyter and IPython Notebooks

Here’s a treasure trove of interesting Jupyter and iPython notebooks, with lots of diverse examples relevant to OPIG, including an RDKit notebook, but also:

Entire books or other large collections of notebooks on a topic (covering Introductory Tutorials; Programming and Computer Science; Statistics, Machine Learning and Data Science; Mathematics, Physics, Chemistry, Biology; Linguistics and Text Mining; Signal Processing; Scientific computing and data analysis with the SciPy Stack; General topics in scientific computing; Machine Learning, Statistics and Probability; Physics, Chemistry and Biology; Data visualization and plotting; Mathematics; Signal, Sound and Image Processing; Natural Language Processing; Pandas for data analysis); General Python Programming; Notebooks in languages other than Python (Julia; Haskell; Ruby; Perl; F#; C#); Miscellaneous topics about doing various things with the Notebook itself; Reproducible academic publications; and lots more!  

 

Using RDKit to load ligand SDFs into Pandas DataFrames

If you have downloaded lots of ligand SDF files from the PDB, then a good way of viewing/comparing all their properties would be to load it into a Pandas DataFrame.

RDKit has a very handy function just for this – it’s found under the PandasTool module.

I show an example below within Jupypter-notebook, in which I load in the SDF file, view the table of molecules and perform other RDKit functions to the molecules.

First import the PandasTools module:

from rdkit.Chem import PandasTools

Read in the SDF file:

SDFFile = "./Ligands_noHydrogens_noMissing_59_Instances.sdf"
BRDLigs = PandasTools.LoadSDF(SDFFile)

You can see the whole table by calling the dataframe:

BRDLigs

The ligand properties in the SDF file are stored as columns. You can view what these properties are, and in my case I have loaded 59 ligands each having up to 26 properties:

BRDLigs.info()

It is also very easy to perform other RDKit functions on the dataframe. For instance, I noticed there is no heavy atom column, so I added my own called ‘NumHeavyAtoms’:

BRDLigs['NumHeavyAtoms']=BRDLigs.apply(lambda x: x['ROMol'].GetNumHeavyAtoms(), axis=1)

Here is the column added to the table, alongside columns containing the molecules’ SMILES and RDKit molecule:

BRDLigs[['NumHeavyAtoms','SMILES','ROMol']]

Viewing 3D molecules interactively in Jupyter iPython notebooks

Greg Landrum, curator of the invaluable open source cheminformatics API, RDKit, recently blogged about viewing molecules in a 3D window within a Jupyter-hosted iPython notebook (as long as your browser supports WebGL, that is).

The trick is to use py3Dmol. It’s easy to install:

pip install py3Dmol

This is built on the object-oriented, webGL based JavaScript library for online molecular visualization 3Dmol.js (Rego & Koes, 2015); here's a nice summary of the capabilities of 3Dmol.js. It's features include:

  • support for pdb, sdf, mol2, xyz, and cube formats
  • parallelized molecular surface computation
  • sphere, stick, line, cross, cartoon, and surface styles
  • atom property based selection and styling
  • labels
  • clickable interactivity with molecular data
  • geometric shapes including spheres and arrows

I tried a simple example and it worked beautifully:

import py3Dmol
view = py3Dmol.view(query='pdb:1hvr')
view.setStyle({'cartoon':{'color':'spectrum'}})
view

py3dmol_in_jupyter_ipython

The 3Dmol.js website summarizes how to view molecules, along with how to choose representations, how to embed it, and even how to develop with it.

References

Nicholas Rego & David Koes (2015). “3Dmol.js: molecular visualization with WebGL”.
Bioinformatics, 31 (8): 1322-1324. doi:10.1093/bioinformatics/btu829