  Diffusion models have emerged as powerful tools for molecular generation,
particularly in the context of 3D molecular structures. Inspired by
non-equilibrium statistical physics, these models can generate 3D molecular
structures with specific properties or requirements crucial to drug discovery.
Diffusion models were particularly successful at learning 3D molecular
geometries' complex probability distributions and their corresponding chemical
and physical properties through forward and reverse diffusion processes. This
review focuses on the technical implementation of diffusion models tailored for
3D molecular generation. It compares the performance, evaluation methods, and
implementation details of various diffusion models used for molecular
generation tasks. We cover strategies for atom and bond representation,
architectures of reverse diffusion denoising networks, and challenges
associated with generating stable 3D molecular structures. This review also
explores the applications of diffusion models in $\textit{de novo}$ drug design
and related areas of computational chemistry, such as structure-based drug
design, including target-specific molecular generation, molecular docking, and
molecular dynamics of protein-ligand complexes. We also cover conditional
generation on physical properties, conformation generation, and fragment-based
drug design. By summarizing the state-of-the-art diffusion models for 3D
molecular generation, this review sheds light on their role in advancing drug
discovery as well as their current limitations.
