  Text-guided molecule generation is a task where molecules are generated to
match specific textual descriptions. Recently, most existing SMILES-based
molecule generation methods rely on an autoregressive architecture. In this
work, we propose the Text-Guided Molecule Generation with Diffusion Language
Model (TGM-DLM), a novel approach that leverages diffusion models to address
the limitations of autoregressive methods. TGM-DLM updates token embeddings
within the SMILES string collectively and iteratively, using a two-phase
diffusion generation process. The first phase optimizes embeddings from random
noise, guided by the text description, while the second phase corrects invalid
SMILES strings to form valid molecular representations. We demonstrate that
TGM-DLM outperforms MolT5-Base, an autoregressive model, without the need for
additional data resources. Our findings underscore the remarkable effectiveness
of TGM-DLM in generating coherent and precise molecules with specific
properties, opening new avenues in drug discovery and related scientific
domains. Code will be released at: https://github.com/Deno-V/tgm-dlm.
