Research Pipeline
Protean Labs organizes peptide discovery as a continuous research pipeline.
Pipeline Shape
- Evidence enters the system.
- Constraints define candidate space.
- Autonomous systems generate candidate fields.
- Validation gates reject weak paths.
- Ranking prioritizes candidates.
- Interpretability layers explain advancement.
- Bounded learning refines future cycles.
- Research packages support internal review.
Evidence To Candidate State
The research pipeline begins by transforming source material into evidence records. Those records inform constraint synthesis, failure memory, and candidate context.
Candidate state is then built across several passes:
- Generation creates a controlled candidate field.
- Validation rejects candidates that fail deterministic gates.
- Feature generation adds sequence descriptors and optional embedding context.
- Scoring ranks candidates across multiple axes.
- Explanations make advancement, warnings, and risks visible.
- Learning signals update future prioritization within limits.
Review Surface
The review layer is designed for scientists and operators who need to understand why a candidate advanced, why it was held, or why it was rejected.
The goal is not volume. The goal is better candidate selection through structured evidence, controlled generation, and accumulated failure memory.
Research Package
A research package is not a clinical claim. It is a review artifact that can contain candidate sequences, rationale, source context, warning burden, feature summaries, failure proximity, and IP-oriented notes for founder review.
The package helps the organization decide what deserves deeper scientific work. It does not replace assays, experimental design, or external validation.