Creat­ing the Future
Projects

Unrav­el­ing univer­sal mecha­nisms of viral replication

Hector Fellow Ralf Bartenschlager
Dr. Leonard Bäcker - Hector RCD Awardee Tessa Quax

This project, led by Prof. Dr. Dr. h.c. Ralf Bartenschlager (University of Heidelberg) and Prof. Dr. Tessa Quax (University of Groningen), investigates whether archaic viruses form specialized replication compartments, a strategy that has already been demonstrated in bacterial and eukaryotic viruses. By combining structural biology, cell biology, medicine, and chemistry, the goal is to identify universal mechanisms of viral replication. Using advanced imaging, genetic labeling, and lipid analysis, the viral replication process in archaea will be studied and compared to other life forms. The results will provide new insights into the evolution of viruses and identify potential approaches for antiviral therapies. In addition, young scientists will be trained in interdisciplinary virology to promote virus research in different biological areas.

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Electrophilic Reactiv­ity Provid­ing Well-Defined Helically Chiral Gold(III) Catalysts for the Asymmet­ric Synthe­sis of Bioac­tive Compounds

Hanock Baiju - Hector RCD Awardee Agnieszka Nowak-Król
Hector Fellow A. Stephen K. Hashmi

The aim of the project led by Hector RCD Awardee Agnieszka Nowak-Król (University of Würzburg) and Hector Fellow A. Stephen K. Hashmi (Heidelberg University) is to develop well-defined helically chiral gold(III) complexes, the first examples of helically chiral gold complexes with gold atoms on either an outer or an inner helicene rim. The catalytic potential of these unprecedented complexes and their practical utility will be demonstrated in the enantioselective synthesis of small organic compounds and biologically or pharmaceutically relevant targets, i.e. natural products and pharmaceutically active compounds.

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Repre­sen­ta­tion Learn­ing and Causal­ity: Theory, Practice, and Impli­ca­tions for Mecha­nis­tic Interpretability

Florent Draye - Hector Fellow Bernhard Schölkopf

This research projec aims to contribute to the development of methods that extract informative and interpretable features from high-dimensional datasets, with a focus on uncovering high-level causally related factors that describe meaningful semantics of the data. This, in turn, can help us gain deeper insights into the representations found within advanced generative models, particularly foundation models and LLMs, with the goal of improving their efficiency and safety.

Representation Learning and Causality: Theory, Practice, and Implications for Mechanistic Interpretability© Florent Draye