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: this https URL

本文介绍了一种基于扩散语言模型的文本导向分子生成方法 (TGM-DLM)，该方法在生成具有特定文本描述的分子时克服了自回归方法的局限性，并通过两阶段的扩散生成过程共同和迭代地更新 SMILES 字符串中的标记嵌入。实验证明，TGM-DLM 模型在生成具有特定属性的连贯且准确的分子方面优于自回归模型 MolT5-Base，无需额外的数据资源，为药物发现和相关科学领域开辟了新的途径。