Unraveling the Potential of Diffusion Models in Small Molecule Generation
It addresses the problem of exploring chemical spaces for drug discovery, but it is incremental as it is a review paper summarizing existing work.
This paper reviews the latest advancements and applications of diffusion models in small molecule generation for drug design, focusing on categorizing methods and comparing their performance on benchmark datasets.
Generative AI presents chemists with novel ideas for drug design and facilitates the exploration of vast chemical spaces. Diffusion models (DMs), an emerging tool, have recently attracted great attention in drug R\&D. This paper comprehensively reviews the latest advancements and applications of DMs in molecular generation. It begins by introducing the theoretical principles of DMs. Subsequently, it categorizes various DM-based molecular generation methods according to their mathematical and chemical applications. The review further examines the performance of these models on benchmark datasets, with a particular focus on comparing the generation performance of existing 3D methods. Finally, it concludes by emphasizing current challenges and suggesting future research directions to fully exploit the potential of DMs in drug discovery.