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BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations 🔥

News

🎉July 18 2024: Happy to share that our enhanced version of BioT5+ ranked 1st place in the Text-based Molecule Generation track and 2nd place in the Molecular Captioning Track at Language + Molecule @ ACL2024 Competition

🔥July 11 2024: Data, codes, and pre-trained models for BioT5+ are relased.

🔥May 16 2024: BioT5+ is accepted by ACL 2024 (Findings).

🔥Mar 03 2024: We have published a suvery paper Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey and the related github repository Awesome-Biomolecule-Language-Cross-Modeling. Kindly check it if you are interested in this field~

🔥Feb 29 2024: Update BioT5 to BioT5+ with the ability of IUPAC integration and multi-task learning!

🔥Nov 06 2023: Update example usage for molecule captioning, text-based molecule generation, drug-target interaction prediction!

🔥Oct 20 2023: The data for fine-tuning is released!

🔥Oct 19 2023: The pre-trained and fine-tuned models are released!

🔥Oct 11 2023: Initial commits. More codes, pre-trained model, and data are coming soon.

Overview

This repository contains the source code for

↓Overview of BioT5

↓Overview of BioT5+

Please refer to the biot5 or biot5_plus folder for detailed instructions.

Citations

BioT5

@inproceedings{pei2023biot5,
  title={BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations},
  author={Pei, Qizhi and Zhang, Wei and Zhu, Jinhua and Wu, Kehan and Gao, Kaiyuan and Wu, Lijun and Xia, Yingce and Yan, Rui},
  booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
  month = dec,
  year = "2023",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2023.emnlp-main.70",
  pages = "1102--1123"
}

BioT5+

@article{pei2024biot5+,
  title={BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning},
  author={Pei, Qizhi and Wu, Lijun and Gao, Kaiyuan and Liang, Xiaozhuan and Fang, Yin and Zhu, Jinhua and Xie, Shufang and Qin, Tao and Yan, Rui},
  journal={arXiv preprint arXiv:2402.17810},
  year={2024}
}

Acknowledegments

The code is based on nanoT5.