ChatGPT-Driven Pathway Discovery in Systems Biology: Innovation, Validation, and Future Horizons

Large language models like ChatGPT are transforming systems biology by accelerating the discovery of novel biological pathways. By synthesizing methodologies from recent peer-reviewed studies, we can see how ChatGPT is being used for hypothesis generation, multi-omics data integration, perturbation response modeling, and metabolic pathway expansion.  

Key Applications and Methodologies:

• Literature-Driven Hypothesis Generation: ChatGPT can mine vast biomedical literature to propose new pathway components. For example, it identified novel proteins for the circadian clock pathway by analyzing undercited studies. However, this approach requires expert oversight to address issues like fabricated references and unsubstantiated suggestions.   
• Multi-Omics Data Integration: ChatGPT facilitates the integration of different omics layers to infer pathways. It has been successfully used to design pipelines for glioblastoma subtyping and to identify novel T-cell exhaustion pathways by correlating chromatin accessibility with surface protein markers in single-cell multi-omics data.
• Perturbation Response Modeling: Benchmarking studies demonstrate ChatGPT's ability to predict pathway responses to interventions, such as drug treatments or gene knockouts, improving prediction accuracy for pathway alterations.
• Metabolic Pathway Expansion: ChatGPT can reconstruct and expand metabolic pathways by augmenting enzyme data with structural information, proposing novel pathways like a laccase-peroxidase synergy pathway for lignin degradation.

Validation and Challenges:

• Experimental Validation: ChatGPT-proposed pathways require experimental confirmation, often through high-throughput screening. For instance, CRISPRi knockdown was used to validate ChatGPT's suggestions in circadian rhythm studies.
• Limitations: Challenges include "hallucinations," where ChatGPT generates incorrect gene interactions, and temporal bias, where models may miss recent discoveries due to their training data cutoff.   
• 
  
• Emerging Solutions and Future Directions:

• Hybrid Human-AI Curation: Combining human expertise with AI can mitigate errors. Human curators can efficiently verify ChatGPT's candidates, achieving comparable timelines to traditional manual review. Interactive tools that allow human-AI collaboration further improve prediction accuracy.
• Domain-Specific Fine-Tuning: Training LLMs on domain-specific biomedical literature, such as BioGPT-X, significantly reduces inaccuracies, particularly in areas like citation accuracy for lncRNA annotations.
• Multimodal Pathway Modeling: Integrating ChatGPT with structural biology data, like AlphaFold2 and Cryo-ET, allows for 3D structural reasoning in pathway modeling, enabling predictions like allosteric binding pockets for drug design.
• Regulatory-Compliant AI: Future development will need to address regulatory guidelines, such as those proposed by the FDA, which mandate pre-registration of training datasets and uncertainty quantification for pathway predictions.

Conclusion:

ChatGPT is revolutionizing pathway discovery by accelerating various stages of research, from hypothesis generation to complex data integration and modeling. While challenges related to accuracy and validation remain, hybrid approaches, fine-tuning, and multimodal integration are promising solutions. The continued evolution of LLMs in synergy with experimental biology holds the potential to unlock the understanding of intricate biological pathways, impacting fields from drug discovery to synthetic biology.