BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations (2310.07276v3)

Abstract: 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 $\href{https://github.com/QizhiPei/BioT5}{Github}$.

• The paper presents BioT5 as a novel pre-training framework integrating molecular, protein, and textual data to overcome limitations in current biological models.
• The framework adopts SELFIES for robust molecule representation and leverages contextual extraction from literature for enhanced bio-entity insights.
• Experimental results show BioT5 outperforming baselines in molecule and protein property prediction, drug-target interaction, and cross-modal generation tasks.

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

The paper entitled "BioT5: Enriching Cross-modal Integration in Biology with Chemical Knowledge and Natural Language Associations" introduces BioT5, a comprehensive pre-training framework designed to enhance the integration of biological data involving molecules, proteins, and textual information. It addresses limitations in current models used for drug discovery, such as the generation of invalid molecular SMILES, underutilization of contextual information, and uniform treatment of both structured and unstructured knowledge.

Key Contributions

BioT5 primarily addresses three significant issues in current biological models:

1. Representation Robustness: BioT5 adopts SELFIES strings for molecule representation instead of the traditional SMILES strings, which ensures robustness as SELFIES provides 100% valid molecular representations.
2. Contextual Knowledge Extraction: By leveraging contextual information surrounding bio-entities in unstructured literature, BioT5 can derive more profound insights into molecular and protein interactions.
3. Differentiation of Knowledge Types: BioT5 makes a clear distinction between structured and unstructured knowledge, enhancing the effective utilization of data from scientific texts and biological databases.

Methodology

BioT5's framework is built upon a multi-task pre-training approach that involves the processing of diverse data sources:

• Data Collection: It consolidates single-modal data (such as molecule SELFIES, protein FASTA, and general text) along with wrapped text where molecule and protein names are represented as their respective sequences. It also utilizes molecule-text and protein-text structured data pairs from databases.
• Pre-training Tasks: The model undergoes various pre-training tasks to assimilate knowledge across different modalities, using both T5 objectives and a translation objective for structured pairs.
• Fine-tuning: BioT5 has been fine-tuned across a spectrum of tasks, including molecule and protein property prediction, drug-target interaction, and cross-modal generation tasks, demonstrating its adaptability and robustness.

Experimental Results

BioT5 achieved superior performance on a range of benchmark tasks:

• Molecule Property Prediction: Demonstrated enhanced performance across MoleculeNet datasets, outperforming various baselines including those utilizing Graph Neural Networks (GNNs) and pre-trained LLMs.
• Protein Property and Interaction Prediction: Outperformed many baselines such as ProtBert and ESM-1b, despite being a smaller model, by efficiently leveraging textual and sequence information, highlighting the impact of cross-modality knowledge integration.
• Cross-modal Generation: In molecule captioning and text-based molecule generation, BioT5 showed notable improvements, achieving exact matches and 100% valid molecule generation due to its robust representation methods.

Implications and Future Work

BioT5's results underscore the potential benefits of integrating textual information with biological data for improved understanding and prediction in bioinformatics and drug discovery. Its methodology can pave the way for future frameworks that incorporate additional biological modalities like genomics or transcriptomics.

Looking forward, sharpening the interpretability of BioT5 and expanding its utility to diverse biological data forms are promising avenues. BioT5 not only offers a robust tool for current tasks but also lays the groundwork for future developments in AI-driven computational biology. Addressing these directions could further refine our understanding of biological systems and facilitate targeted drug discovery.