Product workflow
From therapeutic intent to testable, mechanism-checked peptide families. The shipped app presents a five-workspace progress ribbon as the primary navigation. Every step produces a reviewable artifact, including pathway claims, Lean verification receipts, lineage, and unresolved evidence gaps.
Program
identity panel, progress ribbon, next-best action
Discover
BioScout rail, working set, selected lineage
Evidence
in-silico bundle, claim ledger, Lean audit drawer
Portfolio
Pareto front, active-learning batch, evidence gaps
Handoff
export manifest, LabSpace batch, returned results
01 →
Step 01: Select conditionDefine therapeutic intent or phenotype as the entry point.
in app: Program ▸ setup
Condition diagram for Obesity (ICD-10 E66) Central condition node labelled E66 surrounded by four annotation labels: phenotype, intent, comorbid, and contraindications. ICD-10 E66 (Overweight and obesity); MeSH D009765. Anchored to standard vocabularies. E66 Phenotype: observable clinical traits (HPO terms) used to constrain the search. phenotype Therapeutic intent: agonism, antagonism, allosteric modulation, or biased signalling. intent Comorbidities: co-occurring conditions that adjust target prioritisation and selectivity requirements. comorbid Contraindications: receptor or pathway interactions to avoid (off-target, safety liabilities). contra-ind. ICD-10 · E66
02 →
Step 02: Map pathways & receptorsPathways, receptor families, ligand classes, candidate mechanism claims, contraindications.
in app: Program ▸ target
Pathway and receptor graph for Obesity Directed graph from condition vertex through pathway nodes (incretin, GLP-1 / cAMP, insulin secretion) to four receptor candidates and verifiable mechanism claims: GLP1R, GIPR, GCGR, Y2R. Pathway graph edges: weighted by curated evidence, graph-AI hypotheses, ligand-receptor curation, and later checked by the mechanism verifier. Condition vertex · Obesity Upstream pathway · incretin Direct mechanism · GLP-1 / cAMP Downstream effector · insulin secretion Receptor candidate · GLP1R GLP1R Receptor candidate · GIPR GIPR Receptor candidate · GCGR GCGR Receptor candidate · Y2R Y2R Convergence node: receptors with overlapping endogenous ligands. Obesity cond. Pathway layer: intermediate biological processes linking condition to druggable targets. pathway Receptor layer · primary: GLP1R · Class B GPCR receptor pathway · Obesity
03 →
Step 03: BioScout source systemsEvidence-backed mimicry plans from evolved peptide systems.
in app: Discover ▸ BioScout
BioScout source systems diagram Three source organism rows (Gila monster, amphibian, fish gut) feeding a curated motif library labelled GLP-1 motif. Heloderma exendin-4 lineage: long-acting GLP-1R agonist scaffold. Gila monster Frog skin peptide families with insulinotropic activity. amphibian Teleost proglucagon-derived peptides as alternative scaffolds. fish gut Motif library · GLP-1 motif: curated sequence motifs and pharmacophores indexed from APD3, DRAMP, ConoServer. GLP-1 motif source systems 04 →
Step 04: Seeded evolutionStart from known bioactive peptide families and evolve local analogs with ancestry.
in app: Discover ▸ lineage
Seeded evolution ancestry tree starting from exendin-4 Phylogenetic-style tree from seed peptide exendin-4 branching into analogs of class DL-GLP-A1, with operator history preserved on each branch. Ancestry edges: each branch records the operator applied and the family constraint preserved. Seed · exendin-4: validated parent peptide with known bioactivity. Seed · exendin-4: validated parent peptide with known bioactivity. Branch operator: substitution / cyclisation / N-methylation under family constraints. Branch operator: substitution / cyclisation / N-methylation under family constraints. Analog leaf · DL-GLP-A1-class candidate with full operator history. Analog leaf · DL-GLP-A1-class candidate with full operator history. Analog leaf · DL-GLP-A1-class candidate with full operator history. Analog leaf · DL-GLP-A1-class candidate with full operator history. Seed peptide · exendin-4 exendin-4 Analog · DL-GLP-A1 DL-GLP-A1 ancestry · exendin-4 05 →
Step 05: Visualize peptide–receptor fitStructure-aware 3D review of binding orientation.
in app: Evidence ▸ structure
Peptide–receptor fit diagram for GLP1R · Class B GPCR Receptor scaffold of GLP1R · Class B GPCR with peptide backbone Cα trace of DL-GLP-A1 placed against the pocket; key contact at ECD contact. Receptor scaffold · GLP1R · Class B GPCR Peptide backbone · DL-GLP-A1 Cα trace placed against the receptor pocket. Cα atom · residue 1 of DL-GLP-A1 Cα atom · residue 2 of DL-GLP-A1 Cα atom · residue 3 of DL-GLP-A1 Cα atom · residue 4 of DL-GLP-A1 Cα atom · residue 5 of DL-GLP-A1 Cα atom · residue 6 of DL-GLP-A1 Key contact · ECD contact (predicted distance < 4 Å). ECD contact GLP1R · Class B GPCR 06 →
Step 06: In-silico + mechanism verificationMulti-criteria scoring, pathway reachability checks, Lean audit artifacts, perturbation evidence, and rejection gates pre-wet-lab.
in app: Evidence ▸ audit
In-silico rejection gates for DL-GLP candidates Six horizontal score bars (fit, stab, sol, aggr, synth, tox) with pass/fail labels for each gate. Receptor fit: composite score from docking pose quality and contact-residue agreement. · score 0.91 · passes gate threshold fit pass Stability: predicted resistance to proteolysis and conformational entropy penalty. · score 0.78 · passes gate threshold stab pass Solubility: CamSol / SolubiS-style intrinsic solubility proxy. · score 0.84 · passes gate threshold sol pass Aggregation propensity: Zyggregator / Tango-style β-aggregation score. · score 0.66 · passes gate threshold aggr pass Synthetic feasibility: SPPS coupling-difficulty estimate plus length and modification penalties. · score 0.74 · passes gate threshold synth pass Toxicity prior: ToxinPred-class classifier; failing candidates are gated out. · score 0.38 · passes gate threshold tox pass gates · DL-GLP
07 →
Step 07: Prepare wet-lab batchHand off through LabSpace to capability-matched partners.
in app: Handoff ▸ batch
Wet-lab batch manifest for DL-GLP candidates Manifest table of five candidate IDs with prefix DL-GLP and four assay vials whose heights encode activity readout (EC50 · cAMP assay). Batch manifest: machine-readable handoff (SiLA 2 / Allotrope ADF) with sequences, modifications, and assay plan. Candidate DL-GLP-0421: passes in-silico gates, queued for SPPS synthesis and binding assay. DL-GLP-0421 Candidate DL-GLP-0438: passes in-silico gates, queued for SPPS synthesis and binding assay. DL-GLP-0438 Candidate DL-GLP-0455: passes in-silico gates, queued for SPPS synthesis and binding assay. DL-GLP-0455 Candidate DL-GLP-0461: passes in-silico gates, queued for SPPS synthesis and binding assay. DL-GLP-0461 Candidate DL-GLP-0473: passes in-silico gates, queued for SPPS synthesis and binding assay. DL-GLP-0473 Vial 1: EC50 · cAMP assay readout — height encodes activity. Vial 2: EC50 · cAMP assay readout — height encodes activity. Vial 3: EC50 · cAMP assay readout — height encodes activity. Vial 4: EC50 · cAMP assay readout — height encodes activity. batch manifest
08
Step 08: Receive results & refineWet-lab and perturbation feedback update ranking, mechanism assumptions, and next-assay selection.
in app: Portfolio ▸ feedback
Wet-lab feedback closed loop diagram Closed loop between a wet-lab node returning EC50 · cAMP assay measurements and a re-rank node that updates the surrogate model and selects the next batch. Closed loop: wet-lab measurements update the surrogate model and re-rank candidates (Bayesian / active learning). Wet-lab node · EC50 · cAMP assay returned through LabSpace. EC50 Re-rank node: surrogate model updated with new evidence; acquisition function selects next batch. re-rank closed loop