duongttr/chebi-20-new

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{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",
}