Drug discovery resources for software people

I’ve been digging through some old notes recently, and found a list of resources to share. Some of these are from a while back, but still useful I think, if this is an area of interest to you.

Modern drug design for dummies. A blog by Tomasz Wegrzanowski from 2006, covering the fundamentals of (small molecule) drug design, why it is the way it is, and the stages of design.

There’s also “Drugs: A very short introduction” (2016) which is a “a non-technical account of how drugs work in the body”.

Ligand-Based and Structure-Based Virtual Screening, a presentation (possibly around 2013) by Val Gillet, which outlines computation approaches to finding drugs.

Machine Learning for Drug Discovery in a Nutshell: Part I and Part II. Posts by Stefan Schroedl from 2018, covering some similar ground to “drug design for dummies”, but focused on machine learning applied to this area.

I liked Deep Learning for the Life Sciences,  too. It’s an O’Reilly text from 2019, getting hands on with some of the foundational libraries in Python.

It’s an ever-changing field. Computational approaches streamlining drug discovery, is a 2023 paper that reviews small molecule discovery.

Looking at cancer, “Introduction to Genomics for Engineers” from St. Jude Children's Research Hospital is terrific, mapping  biology to the language of software engineering. There are a few pages marked as WIP, but it’s still very good.  

To that I would add the animations from The Walter and Eliza Hall Institute of Medical Research of biological processes. Some have narration, some don’t. 

Switching away from small molecules to proteins and antibodies, there’s a series of posts in 2024 by Abhishaike Mahajan, starting with “A primer on ML in antibody engineering”.

And finally, you have to follow what DeepMind/Isomorphic Labs and similar are doing: so many interesting tools and ideas coming out from them.