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PhD: Machine learning interatomic potentials for ... (No replies)
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Accurate computational methods for predicting the strength of binding between a candidate drug molecule and its therapeutic target have the potential to revolutionise the drug discovery process. Our goal is to improve the accuracy of this approach by combining free energy calculations with the Gaussian Approximation Potential (GAP), which employs machine learning techniques to faithfully reproduce the quantum mechanical potential energy surface of the drug molecule.
This project will be coordinated by two supervisors with expertise in the machine learning (Prof Gábor Csányi, University of Cambridge) and computer-aided drug design (Dr Daniel Cole, Newcastle University) aspects of this research. The student will work closely with drug discovery programmes at AstraZeneca (Dr Graeme Robb) with the goal of establishing these computational methods as part of the standard tool kit in the drug discovery pipeline.
The project will provide highly sought-after training in the fields of computational medicinal chemistry and machine learning. As such, it will provide excellent experience for a future career in either academia or the pharmaceutical industry.
Funding rules of EPSRC stipulate that applicants must be EU citizens resident in the UK (for at least the last three years). The successful applicant will enter the EPSRC Centre for Doctoral Training in Computational Methods for Materials Science.
Informal enquiries should be directed to Professor Gábor Csányi ([email protected])
Further information on research at Cambridge, Newcastle and AstraZeneca may be found at:
http://www.eng.cam.ac.uk/profiles/gc121
https://blogs.ncl.ac.uk/danielcole/
https://www.astrazeneca.co.uk
https://www.csc.cam.ac.uk/academic/cdtcompmat