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.

翻译：文本引导分子生成是一项根据特定文本描述生成分子的任务。目前，大多数基于SMILES的分子生成方法都依赖于自回归架构。本文提出了一种基于扩散语言模型的文本引导分子生成方法（TGM-DLM），该方法利用扩散模型解决自回归方法的局限性。TGM-DLM通过两阶段扩散生成过程，对SMILES字符串中的词元嵌入进行协同迭代更新。第一阶段根据文本描述对随机噪声进行优化嵌入，第二阶段修正无效SMILES字符串以形成有效的分子表示。实验证明，TGM-DLM在无需额外数据资源的情况下，其性能优于自回归模型MolT5-Base。研究结果凸显了TGM-DLM在生成具有特定属性的一致且精确分子方面的显著效能，为药物发现及相关科学领域开辟了新途径。代码将发布在：https://github.com/Deno-V/tgm-dlm。