---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: CID
    dtype: int64
  - name: SMILES
    dtype: string
  - name: description
    dtype: string
  - name: SELFIES
    dtype: string
  splits:
  - name: train
    num_bytes: 492967130.187
    num_examples: 26407
  - name: validation
    num_bytes: 62689874.499
    num_examples: 3301
  - name: test
    num_bytes: 59531554.9
    num_examples: 3300
  download_size: 584967132
  dataset_size: 615188559.586
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
license: apache-2.0
task_categories:
- text2text-generation
- text-generation
- translation
language:
- en
tags:
- chebi20
- captioning
size_categories:
- 10K<n<100K
---
This dataset was used in paper **Mol2Lang-VLM: Vision- and Text-Guided Generative Pre-trained Language Models for Advancing Molecule Captioning through Multimodal Fusion**
- DOI: https://doi.org/10.18653/v1/2024.langmol-1.12
- GitHub: https://github.com/nhattruongpham/mol-lang-bridge/tree/mol2lang

This dataset contains:
- SELFIES strings (converted by *selfies* package)
- SMILES strings
- Molecular images (converted by *RDKit*)
- Molecular captions.

# Citation
If you use this dataset, please cite to these papers:
```
@inproceedings{tran-etal-2024-mol2lang,
    title = "{M}ol2{L}ang-{VLM}: Vision- and Text-Guided Generative Pre-trained Language Models for Advancing Molecule Captioning through Multimodal Fusion",
    author = "Tran, Duong  and
      Pham, Nhat Truong  and
      Nguyen, Nguyen  and
      Manavalan, Balachandran",
    editor = "Edwards, Carl  and
      Wang, Qingyun  and
      Li, Manling  and
      Zhao, Lawrence  and
      Hope, Tom  and
      Ji, Heng",
    booktitle = "Proceedings of the 1st Workshop on Language + Molecules (L+M 2024)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.langmol-1.12",
    doi = "10.18653/v1/2024.langmol-1.12",
    pages = "97--102",
    abstract = "This paper introduces Mol2Lang-VLM, an enhanced method for refining generative pre-trained language models for molecule captioning using multimodal features to achieve more accurate caption generation. Our approach leverages the encoder and decoder blocks of the Transformer-based architecture by introducing third sub-layers into both. Specifically, we insert sub-layers in the encoder to fuse features from SELFIES strings and molecular images, while the decoder fuses features from SMILES strings and their corresponding descriptions. Moreover, cross multi-head attention is employed instead of common multi-head attention to enable the decoder to attend to the encoder{'}s output, thereby integrating the encoded contextual information for better and more accurate caption generation. Performance evaluation on the CheBI-20 and L+M-24 benchmark datasets demonstrates Mol2Lang-VLM{'}s superiority, achieving higher accuracy and quality in caption generation compared to existing methods. Our code and pre-processed data are available at https://github.com/nhattruongpham/mol-lang-bridge/tree/mol2lang/.",
}

@inproceedings{nguyen-etal-2024-lang2mol,
    title = "{L}ang2{M}ol-Diff: A Diffusion-Based Generative Model for Language-to-Molecule Translation Leveraging {SELFIES} Representation",
    author = "Nguyen, Nguyen  and
      Pham, Nhat Truong  and
      Tran, Duong  and
      Manavalan, Balachandran",
    editor = "Edwards, Carl  and
      Wang, Qingyun  and
      Li, Manling  and
      Zhao, Lawrence  and
      Hope, Tom  and
      Ji, Heng",
    booktitle = "Proceedings of the 1st Workshop on Language + Molecules (L+M 2024)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.langmol-1.15/",
    doi = "10.18653/v1/2024.langmol-1.15",
    pages = "128--134",
    abstract = "Generating de novo molecules from textual descriptions is challenging due to potential issues with molecule validity in SMILES representation and limitations of autoregressive models. This work introduces Lang2Mol-Diff, a diffusion-based language-to-molecule generative model using the SELFIES representation. Specifically, Lang2Mol-Diff leverages the strengths of two state-of-the-art molecular generative models: BioT5 and TGM-DLM. By employing BioT5 to tokenize the SELFIES representation, Lang2Mol-Diff addresses the validity issues associated with SMILES strings. Additionally, it incorporates a text diffusion mechanism from TGM-DLM to overcome the limitations of autoregressive models in this domain. To the best of our knowledge, this is the first study to leverage the diffusion mechanism for text-based de novo molecule generation using the SELFIES molecular string representation. Performance evaluation on the L+M-24 benchmark dataset shows that Lang2Mol-Diff outperforms all existing methods for molecule generation in terms of validity. Our code and pre-processed data are available at https://github.com/nhattruongpham/mol-lang-bridge/tree/lang2mol/."
}
```

```
@inproceedings{edwards-etal-2024-l,
    title = "{L}+{M}-24: Building a Dataset for {L}anguage+{M}olecules @ {ACL} 2024",
    author = "Edwards, Carl  and
      Wang, Qingyun  and
      Zhao, Lawrence  and
      Ji, Heng",
    editor = "Edwards, Carl  and
      Wang, Qingyun  and
      Li, Manling  and
      Zhao, Lawrence  and
      Hope, Tom  and
      Ji, Heng",
    booktitle = "Proceedings of the 1st Workshop on Language + Molecules (L+M 2024)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.langmol-1.1",
    doi = "10.18653/v1/2024.langmol-1.1",
    pages = "1--9",
    abstract = "Language-molecule models have emerged as an exciting direction for molecular discovery and understanding. However, training these models is challenging due to the scarcity of molecule-language pair datasets. At this point, datasets have been released which are 1) small and scraped from existing databases, 2) large but noisy and constructed by performing entity linking on the scientific literature, and 3) built by converting property prediction datasets to natural language using templates. In this document, we detail the L+M-24 dataset, which has been created for the Language + Molecules Workshop shared task at ACL 2024. In particular, L+M-24 is designed to focus on three key benefits of natural language in molecule design: compositionality, functionality, and abstraction",
}
```