PhD student position in the AI Laboratory for Biomolecular Engineering (AIBE) at the Department of Computer Science and Engineering, Chalmers University of Technology.
It is estimated that ~75% of the human proteome lacks deep binding sites and is considered “undruggable” by traditional small molecule inhibitors. Nonetheless, these so-called undruggable targets are implicated in a wide range of diseases, including cancer, autoimmune diseases, and cardio-metabolomic diseases, motivating the development of therapeutic modalities beyond small molecule inhibitors. 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. Nonetheless, they have seen limited integration of high content assay data. DGMs not only enable scientists to delegate error-prone decisions to computers via the use of predictive and generative computational models, but also have the added advantage that they can learn from datasets of billions of molecules in minutes and be regularly updated with new data.
In this project, the candidate will focus on the development of novel AI tools, particularly deep generative models, for the controlled design of therapeutic modalities based on phenotypic profiles. The goal is to develop ML-based de novo design tools which enable researchers to reverse engineer novel bioactive molecules given a desired phenotype. To this end, the candidate will have the opportunity to work with a range of -omics datasets and high-content screening data, as well as to build on the latest developments in machine learning. While the focus of this project is on deep learning and method development, the ideal candidate will also have a keen interest in molecular biology.