BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning
<|reference_start|>BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning: Recent research trends in computational biology have increasingly focused on integrating text and bio-entity modeling, especially in the context of molecules and proteins. However, previous efforts like BioT5 faced challenges in generalizing across diverse tasks and lacked a nuanced understanding of molecular structures, particularly in their textual representations (e.g., IUPAC). This paper introduces BioT5+, an extension of the BioT5 framework, tailored to enhance biological research and drug discovery. BioT5+ incorporates several novel features: integration of IUPAC names for molecular understanding, inclusion of extensive bio-text and molecule data from sources like bioRxiv and PubChem, the multi-task instruction tuning for generality across tasks, and a numerical tokenization technique for improved processing of numerical data. These enhancements allow BioT5+ to bridge the gap between molecular representations and their textual descriptions, providing a more holistic understanding of biological entities, and largely improving the grounded reasoning of bio-text and bio-sequences. The model is pre-trained and fine-tuned with a large number of experiments, including \emph{3 types of problems (classification, regression, generation), 15 kinds of tasks, and 21 total benchmark datasets}, demonstrating the remarkable performance and state-of-the-art results in most cases. BioT5+ stands out for its ability to capture intricate relationships in biological data, thereby contributing significantly to bioinformatics and computational biology. Our code is available at \url{https://github.com/QizhiPei/BioT5}.<|reference_end|>
@article{pei2024biot5+:,
    title={BioT5+: Towards Generalized Biological Understanding with IUPAC
  Integration and Multi-task Tuning},
    author={Qizhi Pei, Lijun Wu, Kaiyuan Gao, Xiaozhuan Liang, Yin Fang, Jinhua
  Zhu, Shufang Xie, Tao Qin, Rui Yan},
    journal={arXiv preprint arXiv:2402.17810},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17810},
    primaryClass={q-bio.QM cs.AI cs.CE cs.LG q-bio.BM}
}