Generative tasks about molecules, including but not limited to molecule generation, are crucial for drug discovery and material design, and have consistently attracted significant attention. In recent years, diffusion models have emerged as an impressive class of deep generative models, sparking extensive research and leading to numerous studies on their application to molecular generative tasks. Despite the proliferation of related work, there remains a notable lack of up-to-date and systematic surveys in this area. Particularly, due to the diversity of diffusion model formulations, molecular data modalities, and generative task types, the research landscape is challenging to navigate, hindering understanding and limiting the area's growth. To address this, this paper conducts a comprehensive survey of diffusion model-based molecular generative methods. We systematically review the research from the perspectives of methodological formulations, data modalities, and task types, offering a novel taxonomy. This survey aims to facilitate understanding and further flourishing development in this area. The relevant papers are summarized at: https://github.com/AzureLeon1/awesome-molecular-diffusion-models.

翻译：分子生成任务（包括但不限于分子生成）对于药物发现和材料设计至关重要，并持续受到广泛关注。近年来，扩散模型已成为一类引人注目的深度生成模型，引发了广泛研究，催生了大量关于其在分子生成任务中应用的成果。尽管相关研究不断涌现，该领域仍缺乏及时且系统的综述。特别是，由于扩散模型建模形式、分子数据模态以及生成任务类型的多样性，研究图景错综复杂，阻碍了领域理解并限制了其发展。为此，本文对基于扩散模型的分子生成方法进行了全面综述。我们从方法建模、数据模态和任务类型等维度系统梳理了现有研究，并提出了一种新的分类体系。本综述旨在促进该领域的理解与进一步繁荣发展。相关论文汇总于：https://github.com/AzureLeon1/awesome-molecular-diffusion-models。