Diffusion Models for Molecules: A Survey of Methods and Tasks

TL;DR

This survey comprehensively reviews diffusion models applied to molecular generation, categorizing methods and tasks to aid understanding and promote future research in drug discovery and material design.

Contribution

It provides the first systematic taxonomy of diffusion-based molecular generative methods, covering diverse formulations, data modalities, and tasks.

Findings

Extensive overview of diffusion models in molecular generation

Identification of key methodological trends and challenges

Compilation of a comprehensive research summary

Abstract

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.