Practical Cheminformatics

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In no particular order, here's a hopefully useful, topical organization of the posts I've written over the past few years. Resources and Reviews A Highly Opinionated List of Open Source Cheminformatics Resources AI in Drug Discovery 2020 - A Highly Opinionated Literature Review Clustering Viewing Clustered Chemical Structures in a Jupyter Notebook Clustering 2.1 Million Compounds for $5 With a Little Help From Amazon & Facebook Self-Organizing Maps - 90s Fad or Useful Tool? (Part 1) Self-Organizing Maps - The Code (Part 2) Molecule Generation Automatic Analog Generation With Common R-group Replacements Predictive Models Predicting Aqueous Solubility - It's Harder Than It Looks Assessing Interpretable Models High-Performance Computing Fast Parallel Cheminformatics Workflows With Dask Wicked Fast Cheminformatics with NVIDIA RAPIDS Databases What Do Molecules That Look LIke This Tend To Do? Adding Chemical Structures to a Recent COVID-19 Drug Repurposing Dataset Filtering ...

In Cheminformatics, we frequently run into cases where we want to look at leader/follower relationships between chemical structures.  For instance, if we've clustered a set of molecules, we might want to start by looking at a table with one example structure for each cluster.  We'd then like to be able to select one or more "interesting" clusters and drill down to the cluster members.  While this is a frequent workflow, I'm not aware of commercially or freely available tools that do a great job of supporting the exploration of leader/follower relationships with chemical structures.  In this post, we'll look at one way of connecting a couple of open source libraries to view cluster representatives and cluster members.  As usual, the code and data associated with this post are available on GitHub .  Rather than trying to explain this more fully, let's consider an example.  In this example, we'll look at a set of 1,495 drugs from the ChEMBL  database...

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In this post, I present an annotated bibliography of some of the interesting machine learning papers I read in 2020.   Please don't be offended if your paper isn't on the list.  Leave a comment with other papers you think should be included. I've tried to organize these papers by topic.  Please be aware that the topics, selected papers, and the comments below reflect my own biases.  I've endeavored to focus primarily on papers that include source code .   Hopefully, this list reflects a few interesting trends I saw this year.  More of a practical focus on active learning Efforts to address model uncertainty, as well as the admission that it's a very difficult problem The (re)emergence of molecular representations that incorporate 3D structure Several interesting strategies for data augmentation Additional efforts toward model interpretability, coupled with the acknowledgment that this is also a difficult problem The application of generative model...