In this lecture , we will discover how drug design is related to biophysics, and in particular structure and function of proteins. We will look at modern classes of drugs, and you need to understand concepts such as "full agonist", "partial agonist", "neutral antagonist", and "inverse agonist". We will talk about the ADME/Tox challenge and Lipinski's rule-of-five (What is that? Look it up if you don't know after the lecture!)

We will look at the strategy of modern (computational) drug design, why it's so important to fail early (if we are going to fail), and different phases of clinical trials. Important early concepts include (virtual) high-throughput screening, QSAR, and docking.

You will see a couple of different techniques for docking, and how docking is conceptually different from the simulations we've looked at before - here speed is everything. You should also know "hit identification" and "lead optimization".

For the last part of the lecture, we will look at some very recent approaches where free energy is increasingly used to optimize the efficiency of drugs, and how simulations are used to identify the kinetics, or e.g. find new binding locations, and we will particularly talk about the important class of G-Protein Coupled Receptors.

Reading instructions

This part is not covered by the book, so follow the lecture and use online resources to find out more!

Study questions

Before lecture 13, I will have you discuss the process & tools of modern drug discovery, with me (hopefully) just providing some comments and reference.

What is the challenge with old-style drug discovery?
How have we improved?
What are the main challenges with the new-style drug discovery?
Where are we heading in the future?