Ligand-based drug design

Published on December 29, 2016   32 min

A selection of talks on Pharmaceutical Sciences

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Hello, I'm Nathan Brown from the Institute of Cancer Research in London, and today I'm going to be talking to you about computational methods in ligand-based drug design for medicinal chemistry.
So ligand-based drug design - it's important to understand what we do and how we achieve it. So we use computational methods to design, select and prioritise synthetic chemistry targets that will then contribute positively to a medicinal chemistry project.
And how we do that is to, starting from the top, to identify potential substituents; if we've got a core scaffold of interest. We then can enumerate libraries in the computer, virtually. From those libraries of possible new chemical structures, we can calculate predictions in the computer using empirical models and first principles models. Then we prioritise compounds, and then the most time-consuming part is the chemical synthesis and biological testing of those target molecules. Followed by data analysis of the resulting data and then we iterate around that to design new, more potent, and more effective drugs.
So an overview of this talk is, we're going to cover molecular representations, a number of different types of molecular descriptors including physicochemical properties and structural fingerprints. We'll then move onto concepts of molecular similarity and how they're used in virtual screening. We'll then conclude with some aspects of statistical learning, in particular unsupervised learning and supervised learning.
So the foundations of modern computational chemistry come from graph theory that was founded as a discipline in the early 18th century by Leonhard Euler, where he tried to understand a path problem in Königsberg where you would cross every bridge in Königsberg only once and pass each of the land masses. So Euler started with a map of Königsberg and then colour coded the landmasses, each landmass is coloured differently, with the bridges connecting them highlighted in blue. So this map already contains a lot of redundant information that isn't necessary for the problem at hand. So we can take away the actual map and we're just left with the morphology of landmasses and the connectivity between these landmasses. Then we can abstract this even further because we don't need to know the morphology of the landmasses, we just need to know that they exist, and they're connected. So we can get rid of morphology and then we end up with an abstract graph representation which led to the foundation of the mathematical discipline of graph theory.