Condition-first peptide discovery without brute-force sequence search.
DiscoveryLab turns therapeutic hypotheses into receptor-aware peptide families, ranks them through in-silico assessment, visualizes peptide–receptor fit in 3D, and hands validated batches to wet-lab partners through LabSpace.
- Search reduction
- 10¹³ → 10²
- per program
- Receptor families
- GPCR · NHR · CK
- examples
- Wet-lab loop
- LabSpace
- feedback-driven
- Incretin signaling0.92
- Melanocortin axis0.81
- Leptin / JAK-STAT0.74
- Adipocyte lipolysis0.61
- DL-FAM-0140.88GLP-1R agonist scaffold · n=32
- DL-FAM-0190.81Dual GLP-1R / GIPR · n=24
- DL-FAM-0220.74MC4R selective · n=18
- Receptor fit0.88
- Stability (predicted)0.71
- Solubility0.83
- Aggregation risk0.18
- Synthesis feasibility0.79
Peptide design fails when the search space is unconstrained.
The number of possible peptides grows exponentially with sequence length. Even short peptides yield combinatorial spaces that no generation strategy can meaningfully cover by sampling alone.
DiscoveryLab treats peptide design as a search-space optimization problem, not a text-generation problem. Generation, ranking, and rejection happen at every stage — under biological, structural, and synthesis constraints.
How DiscoveryLab avoids exponential explosion.
Eight stages, each constraining, ranking, or rejecting candidates. The full peptide universe is never enumerated — only biologically plausible neighborhoods are searched.
Condition-first framing
Researchers begin with a biological condition or desired phenotype. The system maps it to pathways, receptor families, known ligand classes, contraindication concerns, relevant assays, and clinical/preclinical evidence.
Target and receptor narrowing
Instead of searching all possible peptides, DiscoveryLab selects a constrained biological target set — limited to evidence-supported receptors and pathways for the condition.
— Examples only. Coverage depends on available evidence per condition.
BioScout mimicry
Comparative BioScout identifies evolved source systems that already solve related biological problems and produces evidence-backed source-system search plans — not blind candidate generation.
Motif and scaffold constraints
The system extracts or applies pharmacophore hints, conserved motifs, receptor-binding residues, charge and hydrophobicity profiles, cyclization opportunities, secondary-structure propensity, protease resistance, and synthesis feasibility.
Family generation, not single sequences
Candidate families are generated around constrained scaffolds: parent sequence, conservative variants, non-natural amino-acid substitutions, cyclized versions, terminal modifications, stability-enhanced variants, and receptor-selectivity variants.
“The system searches local neighborhoods around biologically plausible scaffolds instead of sampling the full peptide universe.”
Genetic programming and cross-pollination
Successful families evolve under selection pressure from in-silico scores, with bounded mutation, crossover between motif families, scaffold recombination, and ncAA substitution strategies. Candidates failing safety, synthesis, or fit gates are rejected.
“Genetic programming is used as a constrained optimization layer, not as unconstrained random sequence generation.”
In-silico lab assessment
Candidates are scored before any wet-lab handoff: receptor fit, pathway relevance, predicted stability, solubility, aggregation risk, synthesis complexity, off-target concerns, novelty, manufacturability, and assay suitability.
Human review and wet-lab handoff
Only candidates passing evidence and quality gates move to LabSpace: batch creation, destination lab selection, capability matching, pricing/terms visibility, assay request, status tracking, returned wet-lab results, and feedback into candidate ranking.
From therapeutic intent to testable peptide families.
A linear, auditable path from condition to validated wet-lab batch — every stage produces an artifact a researcher can review.
Select condition
Define therapeutic intent or phenotype as the entry point.
Map pathways & receptors
Pathways, receptor families, ligand classes, contraindications.
BioScout source systems
Evidence-backed mimicry plans from evolved peptide systems.
Generate constrained families
Scaffold-bounded candidate families, not single sequences.
Visualize peptide–receptor fit
Structure-aware 3D review of binding orientation.
In-silico lab assessment
Multi-criteria scoring and rejection gates pre-wet-lab.
Prepare wet-lab batch
Hand off through LabSpace to capability-matched partners.
Receive results & refine
Wet-lab feedback updates ranking and search direction.
Structure-aware review for receptors, peptides, and fit.
DiscoveryLab includes RealityKit-based 3D visualization on macOS and visionOS. Inspect receptor structures, peptide conformations, binding orientation, candidate families, and interaction-relevant residues.
- ▢Ribbon / cartoon receptor visualization
- ▢Peptide backbone and side-chain inspection
- ▢Candidate switching across families
- ▢Rotation, zoom, pan with reference axes
- ▢Tap or select molecular elements for annotations
- ▢Future support for AlphaFold DB and imported structural references where available
A peptide tutor backed by retrieval and citations.
DiscoveryLab includes a local peptide tutor backed by retrieval, citations, and quality gates. It supports research reasoning around design choices, receptor rationale, peptide families, uncertainty, contraindication concerns, and evidence gaps.
Dual agonism at GLP-1R and GIPR has shown additive effects on glycemic control and weight loss in published preclinical and Phase 2/3 programs [1][2]. For this condition mapping, family DL-FAM-019 retains GLP-1R primary contact residues while introducing a GIPR-permissive C-terminal motif [3]. Uncertainty: long-term receptor desensitization data is incomplete [4].
Before wet lab: automatic computational triage.
A standardized assessment matrix runs across every candidate. Failing scores constrain or reject; passing scores produce a defensible priority list.
| Assessment | What it checks | Why it reduces search space |
|---|---|---|
| Receptor / pathway relevance | Alignment of candidate to selected receptor and pathway evidence | Discards candidates with no plausible biological target |
| Sequence motif plausibility | Presence and orientation of binding motifs and pharmacophores | Removes scaffolds that violate known structural priors |
| Stability (predicted) | Half-life proxies, protease cleavage liability, oxidation risk | Filters chemically fragile sequences early |
| Solubility | Predicted aqueous solubility under assay-relevant conditions | Avoids candidates that cannot be tested at meaningful concentrations |
| Aggregation risk | β-sheet propensity, hydrophobic patches, self-association cues | Reduces wet-lab failure from precipitation |
| Synthesis feasibility | SPPS difficulty, cyclization route, ncAA availability | Prevents prioritization of impractical sequences |
| Off-target concerns | Predicted cross-reactivity to related receptors and proteins | Surfaces candidates needing selectivity engineering |
| ncAA compatibility | Compatibility of non-natural residues with target chemistry | Keeps v2 ncAA expansion within feasible bounds |
| Assay readiness | Match between candidate and available wet-lab assays | Ensures handoffs are testable as designed |
| Wet-lab priority | Composite ranking across all assessments above | Final gate before LabSpace batch creation |
Connected wet-lab collaboration.
DiscoveryLab prepares candidate batches and uploads them to LabSpace — a shared web workspace for selecting wet-lab partners, reviewing capabilities, sending batches, monitoring status, receiving results, and feeding them back into DiscoveryLab.
- Created in DiscoveryLabNov 04 · 09:12
- Sent to Helix Bio LabNov 04 · 14:30
- Received & QC passedNov 06 · 08:50
- Assay in progressNov 07 · 11:00
- Results returnedNov 12 · expected
Designed for constrained discovery, not infinite generation.
DiscoveryLab reduces a combinatorial sequence universe into ranked, testable neighborhoods.
Naïve peptide generation scales as alphabet_sizeᴸᴱᴺᴳᵀᴴ. DiscoveryLab reduces effective search by lowering the target receptor set, selecting plausible scaffold classes, applying motif constraints, using structural priors, generating candidate families around plausible neighborhoods, pruning with in-silico assays, and updating future search from wet-lab feedback.
“Every stage either constrains, ranks, or rejects. Nothing is generated simply because it is syntactically possible.”
- R₁lower target receptor set
- R₂select plausible scaffold classes
- R₃apply motif & pharmacophore constraints
- R₄structural priors (binding geometry)
- R₅family neighborhoods around scaffolds
- R₆in-silico assay pruning
- R₇wet-lab feedback re-ranking
Research software with explicit uncertainty.
DiscoveryLab is for peptide research, discovery planning, and candidate prioritization. It is not a substitute for regulatory review, clinical judgment, laboratory validation, toxicology, or controlled studies.
- ▢no clinical claims
- ▢no diagnostic use
- ▢no automated prescribing
- ▢evidence gaps surfaced explicitly