Foundations of Cheminformatics and Computational Chemistry Homework #1 #3

Sulstice · 2023-12-19T17:38:40Z

Sulstice
Dec 19, 2023
Maintainer

In today's homework we will be doing a basic statistical analysis to look for behaviour for a set of numbers on a list of molecules.

In Molecular Dynamics or Cheminformatics we most often work with a lot of data and we want to understand the properties on a set of data if we don't know anything else. It's a good technique in gaining an understanding of properties that the data exhibits. To begin, I have a dictionary of SMILES:

molecules = {
       "benzene": "C1=CC=CC=C1",
        "pyridine": "C1=CC=CN=C1",
        "piperidine": "N1CCCCC1",
        "piperazine": "N1CCNCC1",
        "cyclohexane": "C1CCCCC1",
        "oxane": "O1CCCCC1",
        "imidazole": "C1=NC=CN1",
        "pyrrolidine": "C1CCNC1",
}

Our first task is to get a list of molecule weights for the molecules. We are going to be using RDKit, an open source cheminformatic tool and plotly as our graphing library.

To install:

python -m pip install rdkit
python -m pip install plotly
python -m pip install kaleido

To convert to molecular weight:

from rdkit import Chem
import rdkit.Chem.Descriptors as Descriptors

rdkit_molecules = [ Chem.MolFromSmiles(smiles) for chemical_name, smiles in molecules.items() ]
molecular_weights = [  Descriptors.ExactMolWt(molecule) for molecule in rdkit_molecules  ]

Next plot the molecular weights as a probability distribution using Plotly with their figure factory. Some example plotting code

import plotly.figure_factory as ff

fig = ff.create_distplot([
    'Molecular Weight'
], molecular_weights, bin_size=.5, show_hist=True, show_rug=False)

fig.update_layout(legend=dict(itemsizing='constant'))
fig.update_layout(legend=dict(
        orientation="h",
        yanchor="bottom",
        y=1.02,
        xanchor="right",
        x=1,  
        font = dict(family = "Arial", size = 30),
        bordercolor="LightSteelBlue",
        borderwidth=2,
    ),
    legend_title = dict(font = dict(family = "Arial", size = 20))
)
fig.update_traces(bar=dict(width=10))

fig.update_xaxes(
                 ticks="outside",
                 tickwidth=3,
                 tickcolor='black',
                 tickfont=dict(family='Arial', color='black', size=20),
                 title_font=dict(size=46, family='Arial'),
                 title_text=label,
                 ticklen=15,
)

fig.update_yaxes(
                 ticks="outside", 
                 tickwidth=3,
                 tickcolor='black', 
                 title_text='Probability Density',
                 tickfont=dict(family='Arial', color='black', size=50),
                 title_font=dict(size=46, family='Arial'),
                 ticklen=15,
)    

fig.update_layout(
    title_text="Total Distribution",
    title_font=dict(size=44, family='Arial'),
    template='simple_white',
    xaxis_tickformat = 'i',
    bargap=0.2,
    height=600,
    width=1000
)    
fig.write_image('%s.png' % 'distribution_plot')

Here you can play around with the parameters and understanding how to make better plots by changing the line or rug fashion. Take your pick. I do suggest perhaps altering the bin size parameter. Drag and drop your plot in the comments below and I can grade it. Happy Christmas/New Years!