Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

Denoising diffusion models, a class of generative models, have garnered immense interest
lately in various deep-learning problems. A diffusion probabilistic model defines a forward …

There has been considerable recent progress in designing new proteins using deep-
learning methods,,,,,,,–. Despite this progress, a general deep-learning framework for protein …

We propose DiffusionDet, a new framework that formulates object detection as a denoising
diffusion process from noisy boxes to object boxes. During the training stage, object boxes …

Deep-learning methods have revolutionized protein structure prediction and design but are
presently limited to protein-only systems. We describe RoseTTAFold All-Atom (RFAA), which …

Three billion years of evolution has produced a tremendous diversity of protein molecules,
but the full potential of proteins is likely to be much greater. Accessing this potential has …

We introduce DiffRF, a novel approach for 3D radiance field synthesis based on denoising
diffusion probabilistic models. While existing diffusion-based methods operate on images …

Predicting the binding structure of a small molecule ligand to a protein--a task known as
molecular docking--is critical to drug design. Recent deep learning methods that treat …

A popular approach to protein design is to combine a generative model with a discriminative
model for conditional sampling. The generative model samples plausible sequences while …

Generative models, especially diffusion models (DMs), have achieved promising results for
generating feature-rich geometries and advancing foundational science problems such as …