The field of protein design has made remarkable progress over the past decade. Historically,
the low reliability of purely structure-based design methods limited their application, but …

Abstract Information in proteins flows from sequence to structure to function, with each step
causally driven by the preceding one. Protein design is founded on inverting this process …

Protein design often begins with the knowledge of a desired function from a motif which motif-
scaffolding aims to construct a functional protein around. Recently, generative models have …

Combining discrete and continuous data is an important capability for generative models.
We present Discrete Flow Models (DFMs), a new flow-based model of discrete data that …

Despite the central role that antibodies play in modern medicine, there is currently no way to
rationally design novel antibodies to bind a specific epitope on a target. Instead, antibody …

Deep learning approaches have spurred substantial advances in the single-state prediction
of biomolecular structures. The function of biomolecules is, however, dependent on the …

Generative models for 3D drug design have gained prominence recently for their potential to
design ligands directly within protein pockets. Current approaches, however, often suffer …

Enzyme design is a critical area in biotechnology, with applications ranging from drug
development to synthetic biology. Traditional methods for enzyme function prediction or …

Designing ligand-binding proteins, such as enzymes and biosensors, is essential in
bioengineering and protein biology. One critical step in this process involves designing …

The conformational landscape of proteins is crucial to understanding their functionality in
complex biological processes. Traditional physics-based computational methods, such as …