(EPSRC) Simulation of d conformation in solution using machine learning potentials

The University of Manchester


Ligand-based drug design relies on tuning the composition and orientation of a small molecule’s chemical structure to maximise its biological activity. A key element of this design process is the ability to characterise the distribution of shapes of the molecule in aqueous solution. Although computational methods are ideally placed to guide ligand design, the empirical force fields typically underpinning these techniques struggle to accurately model conformations across the large, diverse chemical space of small druglike molecules. This project seeks to draw on recent developments in machine learning (ML) potentials [1-3] to generate models which can capture many-body effects at the QM level of theory in an aqueous environment. The methods will be used for guiding molecular design of druglike compounds in solution, with applications in drug discovery. This project combines to offer an exciting research opportunity at the junction of computational biophysics, drug design and artificial intelligence.

Entry requirements

Candidates are expected to hold (or be about to obtain) a minimum UK Upper First or 2:1 (or equivalent) in a related area / subject. Candidates with an interest in machine learning are encouraged to apply.

How to apply

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/).

Interested candidates must first make contact with the Primary Supervisor prior to submitting a formal application, to discuss their interest and suitability for the project.

On the online application form select PhD Biochemistry (this is for application purposes only).

Equality, Diversity and Inclusion

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full equality, diversity and inclusion statement can be found on the website https://www.bmh.manchester.ac.uk/study/research/apply/equality-diversity-inclusion/

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