扩散模型助力小分子生成，加速药物研发进程
Unraveling the Potential of Diffusion Models in Small Molecule Generation
- 系统梳理扩散模型在分子生成中的理论与应用方法
- 对比多种3D生成方法在基准数据集上的表现
- 适合药物发现与生成式AI研究者参考
生成式AI为药物设计带来新思路，推动对庞大化学空间的探索。扩散模型（DMs）作为新兴工具，近年来在药物研发领域备受关注。本文全面综述了DMs在分子生成中的最新进展与应用，首先介绍其理论基础，随后根据数学与化学应用场景对基于DM的分子生成方法进行分类。进一步评估了这些模型在基准数据集上的性能，尤其聚焦现有3D生成方法的生成效果比较。最后，指出当前挑战并提出未来研究方向，以充分挖掘DMs在药物发现中的潜力。
原文摘要 · Abstract (English)
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.
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