BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations
This work addresses problems in drug discovery and biological research by improving cross-modal integration, though it appears incremental as it builds on existing pre-training methods with specific enhancements.
The paper tackles limitations in biological cross-modal integration, such as invalid molecular SMILES and underutilized context, by proposing BioT5, a pre-training framework that uses SELFIES for 100% robust molecular representations and distinguishes structured and unstructured knowledge, resulting in superior performance across various tasks after fine-tuning.
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 $\href{https://github.com/QizhiPei/BioT5}{Github}$.