BioT5+: Towards Generalized Biological Understanding with IUPAC
  Integration and Multi-task Tuning
Qizhi Pei Lijun Wu Kaiyuan Gao Xiaozhuan Liang Yin Fang Jinhua Zhu Shufang Xie Tao Qin Rui Yan
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
novel 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}.