Unraveling the Potential of Diffusion Models in Small Molecule Generation

TL;DR

This paper reviews recent advancements in diffusion models for small molecule generation, highlighting their theoretical foundations, categorization, performance on benchmarks, and future research directions in drug discovery.

Contribution

It provides a comprehensive overview of diffusion models in molecular generation, including categorization, performance analysis, and identification of challenges and future directions.

Findings

Diffusion models show promising results in molecular generation.

Benchmark datasets reveal strengths and limitations of current DMs.

Future research should address existing challenges to enhance drug discovery applications.

Abstract

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.