**PLEASE NOTE – the deadline for requesting a funding pack from Darwin Trust has now passed and completed funding applications must be submitted to Darwin Trust by 19th January. We can still accept applications for this project from self-funding students.
Predicting which compounds will bind to a drug target (such as an enzyme) and which will not is a challenging problem. Advances in the field of in silico modelling and virtual screening have been made in the last 20 years, but determining the affinity of a small molecule for a protein’s active site without measuring it via ‘wet-lab’ experimentation is still fraught with difficulties. This is unfortunate; the current methods of high-throughput screening used by pharmaceutical companies are extremely expensive and time-consuming. Replacing this approach with an effective virtual process would dramatically bring down the costs of drug discovery, which are currently extraordinarily high (the cost of bringing a drug to market has been estimated to be on the order of $2 billion).
Current software that attempts to predict the position of small molecules bound to a protein’s active site do exist. However, due to historical limitations in computer speed, they do not model the molecules as accurately as they could. As Moore’s law continues to hold, more rigorous modelling algorithms can be implemented to increase the accuracy of the binding predictions. Quantum mechanics in theory provides the means by which atoms and molecules can be modelled, and their behaviour predicted, with complete accuracy.
We propose to introduce aspects of these approaches to the structure-based virtual screening methodologies we have already developed. Our group maintains several collaborations with labs that are able to take the predictions of our software and verify them experimentally. Our previous work has discovered novel inhibitors of Astacins, Immunophilins and several different E3 ligases; we expect that by improving the software used to identify these inhibitors, our success rates can be increased further.
Students will gain knowledge of computational chemistry, molecular modelling, programming, drug design and virtual screening. There may also be scope for learning the lab techniques involved in inhibitor discovery and structural biology. Candidates should have a 2:1 or better Honours degree in a Chemistry- , Biochemistry- or Computer Science-related subject. Familiarity with computers is a must, and some programming knowledge (or a desire to learn it) would be an advantage.
https://www.ed.ac.uk/profile/douglas-houston
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