无需训练即可指导分子图生成，提升生成可控性。
Training-Free Guidance for Discrete Diffusion Models for Molecular Generation
- 提出离散扩散模型的无训练引导框架
- 可精准控制原子类型比例与分子量
- 适合需要快速定制分子结构的研究者
当前连续数据的无训练引导方法发展迅速，使基础扩散模型可与可替换的引导模型结合。然而，离散数据的等效引导方法尚不明晰。本文提出一种将无训练引导应用于离散数据的框架，并在基于DiGress架构的分子图生成任务中验证其有效性。通过搭配返回特定原子类型占比及重原子分子量的引导函数，展示了该方法在生成过程中的可控引导能力。
原文摘要 · Abstract (English)
Training-free guidance methods for continuous data have seen an explosion of interest due to the fact that they enable foundation diffusion models to be paired with interchangable guidance models. Currently, equivalent guidance methods for discrete diffusion models are unknown. We present a framework for applying training-free guidance to discrete data and demonstrate its utility on molecular graph generation tasks using the discrete diffusion model architecture of DiGress. We pair this model with guidance functions that return the proportion of heavy atoms that are a specific atom type and the molecular weight of the heavy atoms and demonstrate our method's ability to guide the data generation.
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