Sifting through the Noise: A Survey of Diffusion Probabilistic Models and Their Applications to Biomolecules

TL;DR

This survey reviews diffusion probabilistic models, highlighting their theoretical foundations and recent advances in biomolecular structure prediction and design, emphasizing their growing importance in computational biology.

Contribution

It provides a comprehensive overview of diffusion models and their applications in biomolecules, summarizing current research and key outcomes in this emerging field.

Findings

Diffusion models are increasingly used in biomolecular prediction and design.

They have achieved significant generative and predictive results.

The survey highlights the theoretical and practical aspects of these models.

Abstract

Diffusion probabilistic models have made their way into a number of high-profile applications since their inception. In particular, there has been a wave of research into using diffusion models in the prediction and design of biomolecular structures and sequences. Their growing ubiquity makes it imperative for researchers in these fields to understand them. This paper serves as a general overview for the theory behind these models and the current state of research. We first introduce diffusion models and discuss common motifs used when applying them to biomolecules. We then present the significant outcomes achieved through the application of these models in generative and predictive tasks. This survey aims to provide readers with a comprehensive understanding of the increasingly critical role of diffusion models.