Ryan McHugh

This paper introduces NA-MPNN, a unified deep learning model for nucleic acid inverse folding, which outperforms existing methods in RNA sequence design and protein-DNA specificity prediction.

This paper introduces a generative diffusion model for de novo design of RNA and nucleoprotein complexes, validated experimentally with SHAPE-seq and electron microscopy.

This paper designs sequence-specific DNA-binding proteins computationally and validates them experimentally, achieving high specificity and affinity for target sequences.

AtomWorks, a modular atom-level data framework, trains structure prediction and design models; the resulting RF3 improves chirality handling and performs between AF3 and Boltz.

This paper introduces RFAA and RFdiffusionAA, models that can predict and design complex biomolecular assemblies including proteins, nucleic acids, small molecules, and covalent modifications.

One RoseTTAFold-derived network models protein-DNA and protein-RNA complexes plus RNA tertiary structure end to end, returning confidence estimates alongside each 3D model.