arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2502.09511cs.LGcs.AIcs.CE
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分子扩散模型:方法与任务综述

Diffusion Models for Molecules: A Survey of Methods and Tasks

Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所);School of Artificial Intelligence, University of Chinese Academy of Sciences(中国科学院大学人工智能学院);Northwestern Polytechnical University(西北工业大学);National University of Singapore(新加坡国立大学);Alibaba DAMO Academy(阿里巴巴达摩院)

Liang Wang, Chao Song, Zhiyuan Liu, Yu Rong, Qiang Liu, Shu Wu, Liang Wang

AI总结:

针对分子生成任务缺乏系统综述的问题,本文从方法公式、数据模态和任务类型角度全面综述基于扩散模型的分子生成方法,提出新颖分类法,助力领域理解与发展。

摘要:

分子生成任务(包括但不限于分子生成)对药物发现和材料设计至关重要,一直备受关注。近年来,扩散模型作为一类令人印象深刻的深度生成模型兴起,引发了广泛研究,推动了其在分子生成任务中的大量应用研究。尽管相关工作激增,但该领域仍缺乏最新且系统的综述。特别是由于扩散模型公式、分子数据模态和生成任务类型的多样性,研究格局难以梳理,阻碍了理解并限制了该领域的发展。为解决此问题,本文对基于扩散模型的分子生成方法进行了全面综述。我们从方法公式、数据模态和任务类型的角度系统回顾研究,提出了一种新颖的分类法。本综述旨在促进对该领域的理解和进一步蓬勃发展。相关论文汇总于:https://github.com/AzureLeon1/awesome-molecular-diffusion-models。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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