Recent advancements in biological research leverage the integration of
molecules, proteins, and natural language to enhance drug discovery. However,
current models exhibit several limitations, such as the generation of invalid
molecular SMILES, underutilization of contextual information, and equal
treatment of structured and unstructured knowledge. To address these issues, we
propose $\mathbf{BioT5}$, a comprehensive pre-training framework that enriches
cross-modal integration in biology with chemical knowledge and natural language
associations. $\mathbf{BioT5}$ utilizes SELFIES for $100%$ robust molecular
representations and extracts knowledge from the surrounding context of
bio-entities in unstructured biological literature. Furthermore,
$\mathbf{BioT5}$ distinguishes between structured and unstructured knowledge,
leading to more effective utilization of information. After fine-tuning, BioT5
shows superior performance across a wide range of tasks, demonstrating its
strong capability of capturing underlying relations and properties of
bio-entities. Our code is available at
$\href{this https URL}{Github}$.

提出了 BioT5，这是一个综合的预训练框架，通过化学知识和自然语言关联来丰富生物学中的跨模态集成，具有 100% 稳健的分子表示，从非结构化生物文献的上下文中提取知识，并区分结构化和非结构化知识，在多种任务中展现出卓越性能，能够捕捉生物实体的潜在关系和属性。

BioT5: 用化学知识和自然语言关联加强生物学中的跨模态整合

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

Commonsense fact verification, as a challenging branch of commonsense
question-answering (QA), aims to verify through facts whether a given
commonsense claim is correct or not. Answering commonsense questions
necessitates a combination of knowledge from various levels. However, existing
studies primarily rest on grasping either unstructured evidence or potential
reasoning paths from structured knowledge bases, yet failing to exploit the
benefits of heterogeneous knowledge simultaneously. In light of this, we
propose Decker, a commonsense fact verification model that is capable of
bridging heterogeneous knowledge by uncovering latent relationships between
structured and unstructured knowledge. Experimental results on two commonsense
fact verification benchmark datasets, CSQA2.0 and CREAK demonstrate the
effectiveness of our Decker and further analysis verifies its capability to
seize more precious information through reasoning.

Decker 是一种应用于常识事实验证的模型，它可以通过发现结构化和非结构化知识之间的潜在关系，实现异构知识的桥接，并实现更精确的理解和推理。实验结果表明，Decker 在两个常识事实验证基准数据集上表现出很高的准确性和能力。