Postdoc position in WASP-funded project at the AI Laboratory for Biomolecular Engineering at Chalmers University of Technology.
About the project
Deep generative models (DGMs) are poised to transform our approach to biomolecular engineering by designing molecules with desired properties from scratch so as to minimize experimental screening. Methods such as DGMs can be integrated with traditional molecular simulation approaches to aid computational chemists in the design and selection of promising new drug candidates. Nonetheless, they have seen limited application to multi-target therapeutic modalities and multi-modal data.
In this project, the candidate will focus on the development of novel AI tools for the accurate prediction of cell permeability in multi-target therapeutic modalities. Such an approach will be vital to the success of next-generation DGMs which are able to engineer cell permeable modalities. To this end, the candidate will have the opportunity to bridge their background in molecular simulations with the latest developments in machine learning. While the focus of this project is on deep learning and method development, the ideal candidate comes from a computational chemistry or related background, and is interested in developing their machine learning expertise through this interdisciplinary research project.