Andrew Kubaney

This paper demonstrates that deep learning can accurately design RNA pseudoknots, achieving results comparable to human experts in a competition.

This paper designs sequence-specific DNA binding proteins using deep learning, achieving a 100-fold improvement in success rates over previous methods.

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.

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.