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

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

Denoising diffusion models embody a type of generative artificial intelligence that can be
applied in computer vision, natural language processing and bioinformatics. In this Review …

Deep generative models have unlocked another profound realm of human creativity. By
capturing and generalizing patterns within data, we have entered the epoch of all …

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

Construction of a scaffold structure that supports a desired motif, conferring protein function,
shows promise for the design of vaccines and enzymes. But a general solution to this motif …

The ability to computationally generate novel yet physically foldable protein structures could
lead to new biological discoveries and new treatments targeting yet incurable diseases …

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 …

Abstract Structure-based drug design (SBDD) aims to design small-molecule ligands that
bind with high affinity and specificity to pre-determined protein targets. Generative SBDD …

Fragment-based drug discovery has been an effective paradigm in early-stage drug
development. An open challenge in this area is designing linkers between disconnected …