Duke University

Sequence-based discrete-diffusion framework that designs peptide binders with specified agonist or antagonist behavior against GPCR targets.

Transcriptomics-native single-cell foundation model that learns batch-invariant cell representations and probabilistically generates virtual cells.

Post-hoc method that restores monotonic scaling to ESM-2 embeddings, yielding Matryoshka-style nested representations for variant effect prediction.

De novo peptide sequencing model that aligns tandem mass spectra with protein language model embeddings through constrained optimization.

De novo peptide binder design framework that targets specific motifs, including disordered regions and conserved epitopes, from target sequence alone.