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Wenchao Weng(翁文超), Hanyu Jiang(蒋涵羽), Xiangjie Kong(孔祥杰), and Giovanni Pau. 2025: Text-guided diverse-expression diffusion model for molecule generation, Chinese Physics B, 34(5): 050701. doi: 10.1088/1674-1056/adbedd
Citation: Wenchao Weng(翁文超), Hanyu Jiang(蒋涵羽), Xiangjie Kong(孔祥杰), and Giovanni Pau. 2025: Text-guided diverse-expression diffusion model for molecule generation, Chinese Physics B, 34(5): 050701. doi: 10.1088/1674-1056/adbedd
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Text-guided diverse-expression diffusion model for molecule generation

  • 1 College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310014, China;

  • 2 Hangzhou Dianzi University ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310018, China;

  • 3 Faculty of Engineering and Architecture, Kore University of Enna, Italy

  • Received Date: 18/11/2024
    Accepted Date: 21/02/2025
  • Fund Project:

    Project supported in part by the National Natural Science Foundation of China (Grant Nos. 62476247 and 62072409), the “Pioneer” and “Leading Goose” R&D Program of Zhejiang (Grant No. 2024C01214), and the Zhejiang Provincial Natural Science Foundation (Grant No. LR21F020003).

  • PACS: 07.05.Kf; 07.05.Mh; 07.05.Tp

  • The task of molecule generation guided by specific text descriptions has been proposed to generate molecules that match given text inputs. Mainstream methods typically use simplified molecular input line entry system (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 self-referencing embedded strings (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. TGDD outperforms both TGM-LDM and the autoregressive model MolT5-Base on most evaluation metrics.
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Text-guided diverse-expression diffusion model for molecule generation

  • Received Date: 18/11/2024
  • Accepted Date: 21/02/2025
Fund Project: 

Abstract: The task of molecule generation guided by specific text descriptions has been proposed to generate molecules that match given text inputs. Mainstream methods typically use simplified molecular input line entry system (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 self-referencing embedded strings (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. TGDD outperforms both TGM-LDM and the autoregressive model MolT5-Base on most evaluation metrics.

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