系统梳理分子生成中扩散模型的方法与应用，助力药物研发。
Diffusion Models for Molecules: A Survey of Methods and Tasks
- 按方法、数据模态、任务类型构建全新分类体系
- 涵盖多种分子生成任务，整合最新研究进展
- 适合从事分子生成与AI制药的研究者参考
分子生成等生成任务在药物发现和材料设计中至关重要，近年来受到广泛关注。扩散模型作为一类强大的深度生成模型，已引发大量研究并应用于分子生成任务。然而，相关工作虽多，仍缺乏系统性综述。由于扩散模型形式多样、分子数据模态各异、生成任务类型繁多，研究领域难以把握，阻碍了发展。为此，本文对基于扩散模型的分子生成方法进行全面综述，从方法论、数据模态和任务类型三个角度进行系统梳理，提出新颖分类体系。本综述旨在促进理解并推动该领域进一步发展。相关论文汇总见：https://github.com/AzureLeon1/awesome-molecular-diffusion-models。
原文摘要 · Abstract (English)
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.
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