100% / Available: 1. December 2020
Two PhD student positions in Computational Drug Discovery
100% / Available: 1. December 2020
The Division of Computational Pharmacy, Department of Pharmaceutical Sciences, is offering two PhD student positions to develop cutting-edge computational methodology and algorithms combining deep neural networks and detailed physicochemical modeling for drug discovery applications.
Computational methods have become an important pillar in structure-based drug design for accelerated identification and optimization of therapeutics. In addition to more traditional, physicochemical methods, recent developments in deep learning offer new directions in computational drug discovery. In our research group, we combine the best of both worlds, i.e. data-driven deep neural networks and detailed physicochemical models.
Your position
Two PhD student positions are available to extend ongoing research on the development of novel algorithms for drug design combining physics-based modeling with deep neural network concepts. The novel methods will be applied to flexible protein-ligand docking and virtual screening methods. You will use algorithms such as deep generative flow-based neural networks, graph convolutional neural networks and molecular simulations to sample the thermodynamic state of dynamic and flexible biomolecules. You will devise an innovative flexible docking pipeline with particular focus on global peptide-protein docking. As part of the project, chemical candidates predicted by our new methods will be experimentally tested by the Molecular Pharmacy group (Prof. Ricklin) at the University of Basel.
Your profile
We offer you
18-03-2024
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