CompChem Tools

Computational Chemistry Tools

Whilst there are number of Open Source computational toolkits and command-line tools they often present a step learning curve for new users. In an effort to provide a simpler environment to access these tools this page will highlight a series of Jupyter notebooks that users can use to run key computational studies that might be undertaken in a drug discovery project.

To use these notebooks you will need to have Jupyter and a number of Python libraries installed. The easiest way to do this is to use Anaconda. Anaconda is a modern package manager and seems to be becoming the preferred source of scientific software.

Install Conda using the instructions here https://www.anaconda.com/distribution/

Then in a terminal window type

conda install jupyter
conda install -c rdkit rdkit
conda install numpy
conda install scipy
conda install scikit-learn
conda install pandas
conda install matplotlib
conda install seaborn

A Jupyter Notebook to aid Docking to MurD protein

This notebook implements a typical protocol for docking ligands to a target protein. It uses RDKit (http://www.rdkit.org) to generate a number of reasonable conformations for each ligand and then uses SMINA (https://sourceforge.net/projects/smina/) to do the docking. Two methods of docking are implemented, the first docks into a rigid receptor, the second sets the protein side-chains around the active site to be flexible. Bear in mind flexible docking will be much, much slower. In the optional final step the resulting docked poses are rescored using a random forest model described in this publication DOI. You can read more details of the notebook here and you can down load a folder containing the notebook and the necessary files here.