BioBridge: Bridging Proteins and Language for Enhanced Biological Reasoning with LLMs
arXiv:2602.17680v1 Announce Type: new Abstract: Existing Protein Language Models (PLMs) often suffer from limited adaptability to multiple tasks and exhibit poor generalization across diverse biological contexts. In contrast, general-purpose Large Language Models (LLMs) lack the capability to interpret protein sequences and fall short in domain-specific knowledge, limiting their capacity for effective biosemantic reasoning. To combine the advantages of both, we propose BioBridge, a domain-adaptive continual pretraining framework for protein understanding. This framework employs Domain-Incremental Continual Pre-training (DICP) to infuse protein domain knowledge and general reasoning corpus into a LLM simultaneously, effectively mitigating catastrophic forgetting. Cross-modal alignment is achieved via a PLM-Projector-LLM pipeline, which maps protein sequence embeddings into the semantic space of the language model. Ultimately, an end-to-end optimization is adopted to uniformly support v
arXiv:2602.17680v1 Announce Type: new Abstract: Existing Protein Language Models (PLMs) often suffer from limited adaptability to multiple tasks and exhibit poor generalization across diverse biological contexts. In contrast, general-purpose Large Language Models (LLMs) lack the capability to interpret protein sequences and fall short in domain-specific knowledge, limiting their capacity for effective biosemantic reasoning. To combine the advantages of both, we propose BioBridge, a domain-adaptive continual pretraining framework for protein understanding. This framework employs Domain-Incremental Continual Pre-training (DICP) to infuse protein domain knowledge and general reasoning corpus into a LLM simultaneously, effectively mitigating catastrophic forgetting. Cross-modal alignment is achieved via a PLM-Projector-LLM pipeline, which maps protein sequence embeddings into the semantic space of the language model. Ultimately, an end-to-end optimization is adopted to uniformly support various tasks, including protein property prediction and knowledge question-answering. Our proposed BioBridge demonstrates performance comparable to that of mainstream PLMs on multiple protein benchmarks, such as EC and BindingDB. It also achieves results on par with LLMs on general understanding tasks like MMLU and RACE. This showcases its innovative advantage of combining domain-specific adaptability with general-purpose language competency.
Executive Summary
The BioBridge framework proposes a novel approach to bridging the gap between protein language models and large language models, enabling enhanced biological reasoning. By employing domain-incremental continual pretraining and cross-modal alignment, BioBridge demonstrates comparable performance to mainstream protein language models and large language models on various tasks, including protein property prediction and general understanding tasks. This innovative framework combines domain-specific adaptability with general-purpose language competency, showcasing its potential in biosemantic reasoning.
Key Points
- ▸ Introduction of the BioBridge framework for protein understanding
- ▸ Employment of Domain-Incremental Continual Pre-training (DICP) for infusing protein domain knowledge
- ▸ Cross-modal alignment via a PLM-Projector-LLM pipeline for mapping protein sequence embeddings
Merits
Improved Adaptability
BioBridge's ability to adapt to multiple tasks and generalize across diverse biological contexts is a significant strength, overcoming the limitations of existing protein language models.
Enhanced Biosemantic Reasoning
The framework's capacity for effective biosemantic reasoning, combining domain-specific knowledge with general-purpose language competency, is a notable advantage.
Demerits
Complexity
The BioBridge framework's reliance on multiple components, including DICP and cross-modal alignment, may introduce complexity and require significant computational resources.
Scalability
The framework's scalability and ability to handle large datasets and diverse biological contexts remain to be thoroughly evaluated.
Expert Commentary
The BioBridge framework represents a significant advancement in the field of bioinformatics, as it addresses the long-standing challenge of bridging the gap between protein language models and large language models. By leveraging the strengths of both approaches, BioBridge demonstrates impressive performance on a range of tasks, from protein property prediction to general understanding tasks. However, the framework's complexity and scalability require further evaluation, and its potential applications must be carefully considered in the context of ethical and policy implications.
Recommendations
- ✓ Further evaluation of BioBridge's performance on diverse biological datasets and tasks to assess its generalizability and robustness.
- ✓ Investigation of the framework's potential applications in biomedical research, including drug discovery and development, and exploration of its implications for biological knowledge discovery and policy-making.
