Tanimoto Similarity Analysis with Python | Python for Chemists

Welcome back to our deep dive into the fascinating world of molecular similarity. In this installment, we continue our exploration using Python to analyze and compare chemical structures. We've covered a lot from scraping chemical data with PubChemPy to creating synonyms and extracting CAS numbers. Now, we're tackling molecular similarity through the mathematical lens of the Tanimoto coefficient. Join us as we demonstrate this concept using Python, comparing naproxen to a range of other molecules to understand their structural and functional relationships. If you're intrigued by the intersection of chemistry and data science, you won't want to miss this detailed walkthrough, including new Python tools that enhance our understanding of chemical fingerprints and similarity metrics. Remember to check out the playlist to follow our series in sequence for a comprehensive learning experience.

[@CJP3]

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[@SR258567]

can you please do this in colab? it would be easy to practice.

[@CJP3]

Let me explore that option! Thanks for the comment :)

[@ukaegbudaniel8361]

is there a way to to this with MACCS fp and Avalon fp

[@CJP3]

Good question, the short see is yes! www.rdkit.org/docs/source/rdkit.Avalon.pyAvalonTools.html

[@louisgonzalez9190]

Do you ever use the
%config InlineBackend.figure_format = 'retina'
Cell magic for plotting high res figures?

[@CJP3]

I didn’t know about that backend render option! Thank you!🙏🏽

[@louisgonzalez9190]

@@CJP3 generally you don't have to worry about pyplot because there is a function for it. It's a life changer for other plotting libraries where it is difficult to increase the dpi such as plotly and plotly looks high res until you save the figure.

[@user-44177]

may you write the whole code?

[@CJP3]

Howdy!! What do you mean?

[@user-44177]

i mean the hash function itself@@CJP3