Readable notes
Reading guide
Summary
This first-author review focuses on how AI-guided approaches can help plant scientists move from sequence, structure, and literature evidence toward better hypotheses about enzyme function and metabolic pathways.
Key themes
- AI can help prioritize candidate enzymes and pathway relationships when experimental testing capacity is limited.
- Sequence, structure, literature, and pathway context are strongest when treated as complementary evidence rather than isolated signals.
- Computational predictions need experimental grounding; the value is in narrowing the search space, not replacing validation.
Technical contribution
This first-author review synthesizes AI-guided strategies for enzyme function prediction, pathway inference, and experimental prioritization in plant metabolism.
Review scope
The review covers computational biology, plant enzymes, metabolism, and AI-assisted biological discovery, with emphasis on using computational evidence to prioritize testable hypotheses.