This tutorial provides a comprehensive survey of methods for fine-tuning diffusion models to
optimize downstream reward functions. While diffusion models are widely known to provide …

Numerous biological and physical processes can be modeled as systems of interacting
entities evolving continuously over time, eg the dynamics of communicating cells or physical …

Despite the exciting progress in target-specific de novo protein binder design, peptide
binder design remains challenging due to the flexibility of peptide structures and the scarcity …

We present a formulation of flow matching as variational inference, which we refer to as
variational flow matching (VFM). Based on this formulation we develop CatFlow, a flow …

AI-driven design problems, such as DNA/protein sequence design, are commonly tackled
from two angles: generative modeling, which efficiently captures the feasible design space …

Diffusion models excel at capturing the natural design spaces of images, molecules, DNA,
RNA, and protein sequences. However, rather than merely generating designs that are …

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 …

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 …

Molecular dynamics (MD) is a powerful technique for studying microscopic phenomena, but
its computational cost has driven significant interest in the development of deep learning …