Diverse-Expression Program

Mainstream methods typically use SMILES to represent molecules and rely on diffusion models or autoregressive structures for modeling. However, the one-to-many mapping diversity when using SMILES to represent molecules causes existing methods to require complex model architectures and larger training datasets to improve performance, which affects the efficiency of model training and generation.

In this paper, we propose a Text-Guided Diverse-Expression Diffusion (TGDD) Model for Molecule Generation. TGDD combines both SMILES and SELFIES into a novel Diverse-Expression molecular representation, enabling precise molecule mapping based on natural language. By leveraging this Diverse-Expression representation, TGDD simplifies the segmented diffusion generation process, achieving faster training and reduced memory consumption, while also exhibiting stronger alignment with natural language.