Jörg K. H. Franke

This study evaluates AlphaFold 3's accuracy and reliability in predicting RNA structures and protein-RNA interfaces, finding that confidence scores do not always correlate with correct predictions.

STRAND improves RNA-protein complex backbone predictions using a diffusion model, building on existing tools like AlphaFold 3 and ProRNA3D-single.

This work introduces a dataset and a transformer-based model for classifying ncRNA-protein interactions, showing strong performance compared to existing methods.

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

This paper introduces a new RNA design paradigm called partial RNA design and presents an algorithm, libLEARNA, that efficiently solves constraint RNA design tasks with variable lengths and precise control over sequence and structure constraints.

RNAinformer designs RNAs from secondary structures by inverting folding algorithms, including non-canonical and tertiary interactions, achieving state-of-the-art performance.

RNAformer predicts RNA secondary structure directly from sequence using a simple axial-attention model, achieving state-of-the-art performance while addressing homology concerns.

RnaBench provides a library for RNA deep learning research, offering curated datasets, benchmarks, and tools to address challenges in RNA structure prediction and design.