Practical Cheminformatics - The Directory

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 Chemical Libraries


Data Visualization / Plotting


A Collection of Things I Frequently Forget How To Do With Seaborn Scatterplots


Interactive Plots with Chemical Structures


Visualizing Chemical Space


Plotting Distributions


Molecular Modeling


Exploring the SARS-CoV-2 Main Protease (MPro) Structures


Building on the Fragments From the Diamond/XChem SARS-CoV-2 Main Protease (MPro) Fragment Screen (Part I)


Building on the Fragments From the Diamond/XChem SARS-CoV-2 Main Protease (MPro) Fragment Screen (Part II) Structure-Base Evaluation of Expanded Fragments


Assigning Bond Orders to PDB Ligands - The Easy Way


Scaffold Hopping? It's Complicated


SAR Analysis


Using the Structure-Activity Landscape Index (SALI) to Analyze Data From the SARS-CoV-2 MPro Screen


Examining the Data From the ChEMBL SARS-CoV-2 Drug Repurposing Screens


Positional Analogue Scanning


Using Reaction Transforms to Understand SAR


Free Wilson Analysis


A Few Updates to Free-Wilson


Model Evaluation / Statistics 


Some Thoughts on Comparing Classification Models


Benchmarking "One Molecular Fingerprint to Rule Them All"


How Good Could (Should) My Models Be?


Multiple Comparisons, Non-Parametric Statistics, and Post-Hoc Tests


Some Thoughts on Evaluating Predictive Models


Getting a Job


How to (Not) Get a Job in Science


How (Not) to Get a Job in Science - Part 2 - The Interview


Philosophical Musings


Where's the code?


Dissecting the Hype With Cheminformatics


AI in Drug Discovery - A Practical View From the Trenches


My Response to Peter Kenny's Comments on "AI in Drug Discovery - A Practical View From the Trenches"


Meeting Notes


Some Notes From the 2018 RDKit UGM


My Boring Life 


My Science/Programming Journey

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