Deep learning has achieved impressive results in various fields such as computer vision
and natural language processing, making it a powerful tool in biology. Its applications now …

We investigate the statistical and computational limits of latent\textbf {Di} ffusion\textbf {T}
ransformers (\textbf {DiT} s) under the low-dimensional linear latent space assumption …

We introduce a refined differentially private (DP) data structure for kernel density estimation
(KDE), offering not only improved privacy-utility tradeoff but also better efficiency over prior …

Generating ligand molecules for specific protein targets, known as structure-based drug
design, is a fundamental problem in therapeutics development and biological discovery …

Proteins are essential for almost all biological processes and derive their diverse functions
from complex 3D structures, which are in turn determined by their amino acid sequences. In …

Recent years have witnessed a surge in the development of protein foundation models,
significantly improving performance in protein prediction and generative tasks ranging from …

The computational prediction and design of peptide binders targeting specific linear
epitopes is crucial in biological and biomedical research, yet it remains challenging due to …

Predicting the change in binding free energy ($\Delta\Delta G $) is crucial for understanding
and modulating protein-protein interactions, which are critical in drug design. Due to the …

Accurate prediction of protein side-chain conformations is necessary to understand protein
folding, proteinprotein interactions and facilitate de novo protein design. Here we apply …

Generative models trained on antibody sequences and structures have shown great
potential in advancing machine learning-assisted antibody engineering and drug discovery …