Christoph Flamm A

The paper uses graph-based cheminformatics and ILP optimization to explore chemical space for carbon fixation pathways, identifying new enzymatic cascades and autocatalytic cycles.

KinPFN approximates RNA folding kinetics using prior-data fitted networks, achieving fast and accurate predictions with synthetic data training.

This paper evaluates deep learning models for RNA secondary structure prediction, highlighting their limitations and showing they cannot fully capture RNA structure properties.

The paper introduces an efficient method to model co-transcriptional RNA-ligand interaction dynamics, exemplified with a riboswitch from Mesoplasma florum.

This paper describes an in silico workflow for designing RNA switches that change structure in response to ligands, using a theophylline aptamer as an example.