University of Virginia

Diffusion-based RNA inverse folding, denoising toward a nucleotide sequence conditioned on a target 3D backbone for higher native sequence recovery.

Single-pass RNA inverse folding: a graph neural network predicts a nucleotide sequence from a target 3D backbone in constant time.

All-atom E(3)-equivariant diffusion model that refines RNA structures by resolving steric clashes and completing missing atoms.

Transformer foundation model for single-cell ATAC-seq that embeds both cells and cis-regulatory elements for annotation and batch correction.

Protein-ligand binding site prediction that ranks pocket residues and pocket center coordinates, staying accurate on AlphaFold-predicted structures.

Binding free energy change (ΔΔG) predictor for protein-protein interfaces, decomposing mutational effects into inverse-folding and energy-model terms.

Attention-based model predicting gene expression from histone modification signals across 56 cell types, with interpretable attention scores.