06-16, 11:00–11:40 (Europe/London), Warwick
Discovering a drug is really hard and expensive; it can take decades to find one, and can fail years into a promising project. Advances in predicting how a protein folds has been at the forefront of the next leap in discovering new medicines, and we're in an age of predicting, with high accuracy, what shape they form and consequently simulating how proteins interact with one another and other chemical entities.
Discovering a drug has always been arduous task, it involves identifying small chemical entities that can be ingested or injected, then dodge a gauntlet of mechanisms intended to neutralise them such as the acid of your stomach and the enzymes predominately in your liver, finally the drug needs to alter some biological process to have a desirable therapeutic outcome.
Computational analysis has been part of the drug discovery process since computers were available; both in the operational aspects of proving a drug works and in analysing the data produced from the effort.
More recently, as the availability of computing has grown and new machine learning techniques have emerged there are exciting prospects that helps us simulate and predict what shape a protein will take and by extension how it will react with other chemical entities.
This talk will give you just enough background to understand what proteins are, how therapeutic outcomes occur when you interact with them and Python and it's libraries can be used to discover new drugs. We'll make reference to AlphaFold 2 and AlphaFold 3, but mostly focus on the practical aspect of how to analyse proteins in python with libraries such biopython, dockstring, rdkit, autodock vina and pymol (all open-source) and how that could be used to find drugs.
No previous knowledge expected
A pharmacologist by training, and a software engineer by vocation. I'm a huge fan of all things that intersect using computers to solve biotech, medtech and healthcare problems.