Constraint Engine
The constraint engine is the layer that makes autonomous peptide generation useful. It narrows design space before candidates are proposed, scored, and reviewed.
Protean treats constraints as an operating surface, not a prompt accessory. Constraints determine which search regions are worth exploring and which candidate paths should be stopped before ranking.
Constraint Domains
- Protease exposure.
- Digestion stability.
- Sequence complexity.
- Charge balance.
- Hydrophobicity.
- Synthesis practicality.
- Novelty and redundancy.
- Failure proximity.
Constraint Sources
Constraints can be shaped by:
- Literature-derived stability and degradation signals.
- Internal failure memory.
- Known stable and unstable examples.
- Sequence-level feature ranges.
- Project-specific research priorities.
- Experimental planning requirements.
The constraint engine does not treat all signals equally. Source quality, evidence type, failure proximity, and review confidence affect how strongly a signal should influence candidate space.
Gate Behavior
Candidates can be stopped before scoring when they violate hard constraints. Other signals become soft penalties, warnings, or ranking context.
Hard gates protect the platform from invalid or obviously weak candidates. Soft constraints preserve nuance for scientific review.
Design Philosophy
Protean does not treat generation as discovery by itself. Candidate generation is only valuable when constrained by scientific objectives and evaluated against failure patterns.
The engine turns research priorities into operational boundaries. Candidates that do not respect those boundaries are stopped early.