Many proteins exert their function by switching among different structures. Knowing the
conformational ensembles affiliated with these states is critical to elucidate key mechanistic …

Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …

The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of
proteins and their interactions, enabling a huge range of applications in protein modelling …

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 …

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 …

We propose a simple, efficient, yet powerful framework for dense visual predictions based
on the conditional diffusion pipeline. Our approach follows a" noise-to-map" generative …

The design of novel protein structures remains a challenge in protein engineering for
applications across biomedicine and chemistry. In this line of work, a diffusion model over …

We develop a framework for non-asymptotic analysis of deterministic samplers used for
diffusion generative modeling. Several recent works have analyzed stochastic samplers …

Biocatalysis harnesses enzymes to make valuable products. This green technology is used
in countless applications from bench scale to industrial production and allows practitioners …

Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …