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 de novo design of multi-target therapeutic modalities from phenotypic screens. Such an approach is an essential first step towards bridging the gap between phenotypic- and target-based drug discovery using DGMs. To this end, the candidate will have the opportunity to connect their previous experience in phenotypic screening 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.