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Technical credibility — evidence, calibration, and claims.
The technical credibility surface: what evidence, calibration, and claims DiscoveryLab stands on.
Technical credibility
Designed for constrained discovery, not infinite generation.
01Condition-first target selection
02Receptor-conditioned generation
03BioScout mimicry from evolved peptide systems
04Structured candidate families, not single sequences
05v2 non-natural amino-acid support
06Genetic programming with bounded mutation/crossover
07Scientific claim-boundary ledger across every assay capability and result
08Public calibration import pipeline for ChEMBL, BindingDB, and PDBbind rows
09Protein-language-model task-head replacement plan for descriptor assays
10ESM-2 3B embeddings generated for all 221 repository candidates
11Boltz-1 / AWS HealthOmics complex-prediction request and contact-map preview
12Pareto-front and active-learning next-batch design
13Wet-lab prep package with 10 selected candidates, 13 synthesis specs, 10 assay protocols, and return template
14In-silico scoring gates at every stage
15Pathway mechanism verification: reachability, blockade, safety, conservation
16Lean 4 audit artifacts and external verification receipts
17PathwayLean reusable package plan with checksums and file list
18Perturbation evidence scoring against encoded assumptions
19RealityKit molecular review (macOS / visionOS)
20Five-workspace progress ribbon as the only app navigation
21Keychain-backed LabSpace notification token handling
22LabSpace wet-lab feedback loop
23Recursive Discovery Loop with improvement proposals, regression gates, approval receipts, and rollback
24Local tutor fallback plus Bedrock/Kendra grounding contract
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Related on this track
Experiments
Validation and assay planning.
AI Scientist
Research loop and next-action reasoning.
Latest results
BioFoundation, wet-lab prep, and Bio-JEPA run.
Agents
Agent-facing operational surface.
Architecture
System layers and contracts.
Modality router
Modalities and enforced claim discipline.
Gut–vagus graph
Curated peptide/receptor seed for the vagal axis.
Operations
Run, review, and inspect surfaces.
LabSpace
Partner handoff and returned results.
Snapshot
Observation half of the improvement loop.
Learning loop
Bounded recursive evidence improvement.
Mathematical framing
Why the model is bounded.
Safety
Research-use boundaries.
Demo
Final call to action.
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