Research volume is not the same as research clarity. The long-term goal is to build an evidence-intelligence layer that helps researchers see the maturity, quality, gaps and direction of peptide research without pretending that an algorithm can replace critical appraisal.
Create a continuously expandable structured map of peptide-related literature with traceable links back to original sources.
→Identify where high preclinical activity is not matched by robust human studies, safety reporting or controlled evidence.
→Surface compounds, mechanisms and indications where the balance of evidence suggests meaningful questions for further investigation.
→Make model-assisted extraction inspectable through schemas, confidence scoring, source traceability, validation and reproducible processing.
→The current site now exposes peptide-by-peptide evidence profiles and study-level search from 6,240 structured records. The next layer is deeper peptide–indication prioritisation, publication timelines, safety-coverage analysis and translational-gap scoring.
The ambition is not automated prescribing. It is a research intelligence environment that can help answer questions such as: where is the evidence dense, where is it weak, what has changed recently, and what deserves deeper human review?
Every derived insight should be traceable to a source publication and processing stage.
Confidence and missing information should be visible rather than hidden behind a single score.
AI accelerates extraction and prioritisation; researchers remain responsible for interpretation and critical appraisal.