Papers › BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and...
BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
Qizhi Pei, Wei zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingce Xia, Rui Yan
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 𝐁𝐢𝐨𝐓5, a comprehensive pre-training framework that enriches cross-modal integration in biology with chemical knowledge and natural language associations. 𝐁𝐢𝐨𝐓5 utilizes SELFIES for 100 robust molecular representations and extracts knowledge from the surrounding context of bio-entities in unstructured biological literature. Furthermore, 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 $\href{https://github.com/QizhiPei/BioT5}{Github}$.
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