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It can take 10-15 years on average to get a drug from lab to the pharmacy and can cost over $1.3 billion. How can we improve this lab-intensive, expensive process? Structure-based drug design and computational approaches have been pivotal. Join us to learn about the modern approaches that lead to successful drug development to allow more targeted therapeutics to improve patients’ quality of life.
Structure-based drug discovery: A fast-track to clinical success?
Olivia Gittins
(PhD Student)
Structure-based drug discovery (SBDD) utilises the 3D structure of protein targets to guide the design of drug compounds. This commonly involves using techniques such as X-ray Crystallography and Cryo-Electron Microscopy to obtain this structural information. SBDD has played a key role in developing several drugs through to clinic, benefitting millions of patients worldwide.
Going forward, a blend of ever improving experimental techniques with emerging AI solutions to the protein folding problem will further expedite the drug discovery process, bringing about meaningful clinical impact.
Going forward, a blend of ever improving experimental techniques with emerging AI solutions to the protein folding problem will further expedite the drug discovery process, bringing about meaningful clinical impact.
How to use supercomputers and machine learning to design anti-viral drugs
Ben Cree
(PhD Student)
Designing drugs is hard and expensive, and so novel computer based approaches (e.g. supercomputers) can be used as both a cheaper, and faster alternatives to traditional wet chemistry. Potential drug molecules can be designed, simulated and scored - all for the price of the electricity used to power the computers. Modern hardware, and advances in machine learning have made these in silico approaches invaluable to the pharmaceutical industry as well as academia.
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