Exploratory Data Analysis With mols2grid and Bemis-Murcko Frameworks

One of the most common tasks in Cheminformatics is exploratory data analysis (EDA).  Given a new dataset, we often need to rapidly explore the chemistry in a set containing hundreds, or even thousands, of molecules.  One useful technique for EDA is the Bemis-Murcko framework.  This technique, originally published by Guy Bemis and Mark Murcko, provides a simple but elegant means of grouping molecules.  Bemis-Murcko frameworks (also known as scaffolds) are created by successively removing monovalent atoms until only ring atoms and linker atoms remain.  There are a few nuances having to do with the removal of exocyclic double bonds and the maintenance of aromaticity, but the method itself is very easy to understand.  There are two versions of the Bemis-Murcko framework, which are sometimes confused.  In the first version, illustrated in the top row of the figure below, monovalent atoms are removed until only ring atoms and linker atoms remain.  In the second version, a generic framework is generated by transforming the result of the previous procedure by converting all atoms to carbon atoms and all bonds to single bonds.  

While both methods can be useful,  I tend to use the first method, which maintains the atom types and bond orders from the input structure.   

In the Jupyter notebook associated with this post, we take a set of structures from the ChEMBL database, calculate the Bemis-Murcko frameworks, then use mols2grid to display the frameworks.  By itself, this would be kind of cool, but Cedric Bouysset made some updates to mols2grid to make this even cooler.  In the latest version of mols2grid, we can create a callback function that is called whenever the user clicks on a structure in the grid.  In his talk at the RDKit User Group Meeting, Cedric showed a nice example where clicking on a molecule in the grid displays a 3D structure below the grid using py3Dmol.  I modified Cedric's code a bit to enable a click on the grid to display another grid.  This provides an easy way to look at things like scaffold->molecule or cluster center->cluster member relationships.  



The notebook also shows how we can use SMARTS to search the grid or the underlying dataframe.   I've found this sort of workflow to be incredibly useful when I'm exploring a new dataset or trying to understand a set of assay results.  Hopefully, you'll find this useful too. 

The Jupyter notebook can be found here on GitHub or can be run directly on Google Colab. 





Comments

Post a Comment

Popular posts from this blog

AI in Drug Discovery 2023 - A Highly Opinionated Literature Review (Part I)

Image
Here’s the first part of my review of some interesting machine learning (ML) papers I read in 2023.  As with the previous editions , this shouldn’t be considered a comprehensive review.  The papers covered here reflect my research interests and biases, and I’ve certainly overlooked areas that others consider vital.  This post is pretty long, so I've split it into three parts, with parts II and III to be posted in the next couple of weeks.    I. Docking, protein structure prediction, and benchmarking II. Large Language Models, active learning, federated learning, generative models, and explainable AI III. Review articles 2023 was a bit of a mixed bag for AI in drug discovery.  Several groups reported that the deep learning methods for protein-ligand docking weren’t quite what they were initially cracked up to be.  AlphaFold2 became pervasive, and people started to investigate, with mixed success, the utility of predicted protein structures.  There were reports of significant advanc
Read more

Generative Molecular Design Isn't As Easy As People Make It Look

Image
I was taken aback by a recent CNBC article entitled “ Generative AI will be designing new drugs all on its own in the near future ”.  I should know better than to pay attention to AI articles in the popular press, but I feel that even scientists working in drug discovery may have a skewed perception of what generative AI can and can’t do.  To understand exactly what’s involved, it might be instructive to walk through a typical generative molecular design workflow and point out a few things.  First, these programs are far from autonomous.  Even when presented with a well-defined problem, generative algorithms produce a tremendous amount of nonsense.  Second, domain expertise is essential when sifting through the molecules produced by a generative algorithm.  Without a significant medicinal chemistry background, one can’t make sense of the results.  Third, while a few nuggets exist in the generative modeling output, a lot of work and good old-fashioned cheminformatics are required to ext
Read more

AI in Drug Discovery - A Highly Opinionated Literature Review (Part II)

Image
Picking up where we left off in Part I , this post covers several other ML in drug discovery topics that interested me in 2023.  Some areas, like large language models, are new, and most of the work is at the proof-of-concept stage.  Others, like active learning, are more mature, and several groups are starting to explore nuances of the methods.   Here’s the structure of Part II.  4. Large Language Models 5. Active Learning 6. Federated Learning 7. Generative Models 8. Explainable AI 9. Other Stuff 4. Large Language Models The emergence of GPT-4 and ChatGPT brought considerable attention to large language models (LLMs) in 2023.  In November and December, several large pharmas held “AI Day” presentations featuring LLM applications for clinical trial data analysis. Many of these groups demonstrated the ability of LLMs to ingest large bodies of unstructured clinical data and subsequently generate tables and reports based on natural language queries.  Aside from some very brief demos on co
Read more