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.

扩散模型在生物分子结构、序列的预测和设计方面的应用所取得的重要成果以及在生成和预测任务中的角色。

筛选噪声：扩散概率模型及其在生物分子中的应用调查

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

Graph Machine Learning (GML) is receiving growing interest within the
pharmaceutical and biotechnology industries for its ability to model
biomolecular structures, the functional relationships between them, and
integrate multi-omic datasets - amongst other data types. Herein, we present a
multidisciplinary academic-industrial review of the topic within the context of
drug discovery and development. After introducing key terms and modelling
approaches, we move chronologically through the drug development pipeline to
identify and summarise work incorporating: target identification, design of
small molecules and biologics, and drug repurposing. Whilst the field is still
emerging, key milestones including repurposed drugs entering in vivo studies,
suggest graph machine learning will become a modelling framework of choice
within biomedical machine learning.

本文综述了生物医药行业中越来越受关注的图机器学习对于模拟生物分子结构、它们之间的功能关系以及综合多组学数据的能力，并在药物研发和发现的全过程中，从靶点识别、小分子和生物类药物设计、以及药物重药用等方面进行了归纳总结，并指出，尽管这一领域仍在崛起，但关键的里程碑已经出现，表明图机器学习将成为生物医学机器学习的首选模型框架。