LOOPER: Machine learning for druggable protein conformations

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This project aims at combining molecular dynamics (MD) simulation and machine learning (ML) techniques to predict how proteins move, and to improve the way drug-like molecules can target flexible protein regions. This builds upon the expertise of the Degiacomi group on generative neural networks (GNNs) and that of the Mey group in method development for MD simulations and drug design. The student will address the questions of: can a GNN be used for generating biologically relevant protein conformations? How can the ensemble of structures then be used for improved design of drug-like molecules that specifically target their IDRs? These questions are central to understanding neurodegenerative diseases such as Parkison's and Alzheimer's, as well as the molecular mechanisms behind antimicrobial resistance. A three-month placement at Redesign Science (www.redesignscience.com) will enable the student to apply their acquired MD and ML techniques to get insights into an active drug discovery project, and learn how to streamline their work in accordance with industry timescales.