• Biotech Valuation

  In today’s post I’ll be looking at biotech valuation and playing around with research agents. I’ve always been interested in the business of biotech, and this was a great opportunity to see if agents can handle some of the heavy lifting involved in company valuation. I should note at the outset that I am in no way an investment or clinical trial professional, and all of this is merely for illustrative purposes.

• Matching Molecular Series with MatchMolSeries.py

  “What else can we put on that position to improve potency?” If you’ve worked in small molecule discovery, you’ll probably have heard some variation of that question before. Replacing R-groups tends to be synthetically much easier than modifying the scaffold of a compound, and it can often dramatically modulate not only the potency of a compound but also its other properties. It should come as no surprise then that executing libraries of R-group variations has become a staple of modern drug discovery. Such libraries result in rich data sets where a position is exhaustively varied but everything else is kept constant. Matched Molecular Series (MMS) were introduced by Wawer and Bajorath in 2011 as a way to transfer knowledge from existing datasets to accelerate discovery. In this post we’ll be implementing Matched Molecular Series in python.

• Free-Wilson edge-cases

  I’ve always been a fan of Free-Wilson Analysis. It’s just such a useful tool: it’s easy-to-use, it helps you understand SAR better and it generates all potentially interesting combinations of your compounds that you might have missed. What’s not to love? In this post I’m going to address a few edge cases that the current code I’m using (written by the fantastic Pat Walters) can use a hand with.