| name | bio-discovery |
| description | Agent-native drug discovery pipeline. Takes a disease name or target, walks Open Targets + ChEMBL + AlphaFold, and produces a .holo scene with hash-verifiable protein structure + known-active compounds + binding-site annotations. End-to-end validated (pipeline probe 2026-04-17).
|
| argument-hint | [disease <name>|target <gene>|compound <name>] [--approved-only] [--top <n>] |
| disable-model-invocation | false |
| allowed-tools | Bash, Read, Write, Edit, Grep, Glob, Task, Agent, WebFetch |
| context | fork |
| agent | general-purpose |
| project-dir | C:/Users/Josep/Documents/GitHub/HoloScript |
/holoscript:bio-discovery — Drug Discovery Pipeline
Command: $ARGUMENTS
Purpose
Bridges the bio-research MCP plugin (biorxiv + ChEMBL + Open Targets) to the HoloScript AlphaFold plugin + simulation contract. Output is a .holo scene that any agent or human can replay bit-identically, with every data source cited and hashed.
This is the concrete implementation of the Verifiable Digital Twin for Drug Discovery positioning (see docs/strategy/positioning-verifiable-digital-twin.md and docs/strategy/drug-discovery-flagship.md).
The Pipeline
user input (disease | target | compound)
↓
[1] Open Targets: search_entities → resolve to IDs
- Disease → EFO_xxxxxxx
- Target → ENSG_xxxxxxxxxxx
- Drug → CHEMBL_xxxxxxx
↓
[2] Open Targets GraphQL: target-disease association (if both given)
- Evidence types, association score, literature count
↓
[3] ChEMBL target_search: resolve to CHEMBL target ID
- Gene symbol → CHEMBL_xxx + UniProt accession
- Target type SINGLE PROTEIN for highest confidence
↓
[4] ChEMBL get_bioactivity: top potent compounds
- IC50/Ki filtered by min_pchembl (default: >= 7, <= 100nM)
- Limit to drug-like compounds (Ro5 compliant if possible)
↓
[5] ChEMBL compound_search: approved drugs for the target
- max_phase=4 for market-approved therapeutics
↓
[6] AlphaFold plugin: fetch/compile protein structure
- UniProt → sequence → @protein_structure trait
- pLDDT scores → @confidence_map trait
- Known binding pocket residues → @binding_site trait
↓
[7] biorxiv search_preprints (optional, with --with-preprints):
- Top recent preprints citing the target
- Added as metadata.recent_research[]
↓
[8] Emit .holo scene with:
- @protein_structure (target)
- @molecule (each bioactive compound)
- @binding_site (affinity from bioactivity data)
- @contract { deterministic, hash_verify, provenance_chain }
- metadata.provenance_source = [chembl-34, open-targets-YY.MM, alphafold-v4]
Subcommands
disease <name> — disease → targets → compounds
/holoscript:bio-discovery disease "non-small cell lung cancer"
Resolves disease EFO ID, queries Open Targets for top associated targets, picks highest-score target, runs full ChEMBL pipeline, emits .holo scene.
target <gene-symbol> — target → compounds
/holoscript:bio-discovery target EGFR
Skips disease resolution, goes straight to target. Use when you know the biology.
compound <name> — compound → target + binding
/holoscript:bio-discovery compound osimertinib
Finds the compound in ChEMBL, looks up its mechanism of action + primary target, emits a scene centered on the binding interaction.
Flags
| Flag | Effect |
|---|
--approved-only | Filter compounds to max_phase=4 only |
--top <n> | Return top N compounds by pChEMBL (default 5) |
--with-preprints | Include bioRxiv preprints from the last 90 days |
--output <path> | Write .holo to specific path (default: examples/bio-discovery/<target>.holo) |
--contract <tier> | Simulation trust tier: discipline / co-location / construction (default) |
Example Output (probed 2026-04-17, EGFR/NSCLC/osimertinib)
composition "EGFR NSCLC — Osimertinib Binding Study" {
metadata {
generator: "holoscript:bio-discovery"
generated_at: "2026-04-17T02:30:00Z"
disease: {
label: "Non-small cell lung cancer"
efo_id: "EFO_0003060"
}
target: {
label: "Epidermal growth factor receptor"
chembl_id: "CHEMBL203"
ensembl_id: "ENSG00000146648"
uniprot: "P00533"
organism: "Homo sapiens"
}
provenance_source: [
"chembl-34",
"open-targets-24.09",
"alphafold-v4"
]
}
object "EGFRKinase" {
@protein_structure(
uniprot: "P00533",
domain: "kinase", // residues 696-1022
name: "EGFR Kinase Domain"
)
@confidence_map(scheme: "viridis", threshold: 70.0)
}
// Approved drug — first-line for EGFR T790M-mutant NSCLC
object "Osimertinib" {
@molecule(
chembl_id: "CHEMBL3353410",
smiles: "C=CC(=O)Nc1cc(Nc2nccc(-c3cn(C)c4ccccc34)n2)c(OC)cc1N(C)CCN(C)C",
name: "Osimertinib",
generic: "AZD9291",
brand: "Tagrisso",
mw: 499.62,
atc: "L01EB04",
max_phase: 4,
first_approved: 2015
)
}
// Top potent compound from ChEMBL (IC50 = 0.45 nM, pChEMBL 9.35)
object "TopHitCHEMBL304271" {
@molecule(
chembl_id: "CHEMBL304271",
ic50_nm: 0.45,
pchembl: 9.35,
assay: "CHEMBL674637", // EGF-receptor kinase tyrosine phosphorylation inhibition
reference: "Bioorg Med Chem Lett 2002 (CHEMBL1134862)"
)
}
binding "EGFR-Osimertinib" {
@binding_site(
structure: "EGFRKinase",
residues: [797, 800, 858], // C797S / T790M / L858R mutation hotspots
ligand: "Osimertinib",
affinity_class: "approved_TKI"
)
}
// Trust by Construction (W.GOLD.013) — Tier 3
contract {
trust_tier: "construction"
deterministic: true
replay_seed: 42
solver_hash_verify: true
provenance_chain: true
hash_on: ["structure", "molecule", "binding"]
}
}
Setup
ENV_FILE="${HOME}/.ai-ecosystem/.env"
[ ! -f "$ENV_FILE" ] && ENV_FILE="/c/Users/Josep/.ai-ecosystem/.env"
set -a && source "$ENV_FILE" 2>/dev/null && set +a
Verified Pipeline Nodes (2026-04-17)
| Stage | Tool | Example result (verified) |
|---|
| Disease ID | mcp__plugin_bio-research_ot__search_entities | "non-small cell lung cancer" → EFO_0003060 ✓ |
| Target ID | mcp__plugin_bio-research_ot__search_entities | "EGFR" → ENSG00000146648 ✓ |
| ChEMBL target | mcp__plugin_bio-research_chembl__target_search | gene_symbol=EGFR → CHEMBL203 (SINGLE PROTEIN) ✓ |
| Bioactivities | mcp__plugin_bio-research_chembl__get_bioactivity | CHEMBL203 + min_pchembl=8.5 → 2,867 total, top 5 with IC50 0.45–2.8 nM ✓ |
| Approved drug | mcp__plugin_bio-research_chembl__compound_search | "osimertinib", max_phase=4 → CHEMBL3353410 with SMILES ✓ |
| Protein structure | @holoscript/alphafold-plugin | UniProt P00533 → pdb_data + mean_plddt ✓ |
Guarantees
This skill produces Verifiable Digital Twin output (W.GOLD.013):
- Bit-identical replay — any third party with the same
.holo file reproduces the same hash-verified simulation
- Cited provenance — every data point carries source DB + version + accession ID
- Audit trail — the
contract { provenance_chain: true } block logs every tool call made during scene generation
- Browser-native — generated scenes compile to WebGPU targets, no install required for reviewers
- Vendor-neutral — data source versions are recorded; if ChEMBL updates, old scenes still replay using their pinned version
Verticals Unlocked
Per docs/strategy/positioning-verifiable-digital-twin.md:
- Drug discovery reproducibility — FDA/EMA submissions reference simulation artifacts by hash
- Pharma preprint verification — agents auto-verify compound claims against ChEMBL
- Academic research — free tier unlocks simulation-first science for labs priced out of Schrödinger / OpenEye
- Regulatory replay — a reviewer clicks a URL, sees the exact simulation
When NOT to Use
- Novel target not in ChEMBL — pipeline falls through; use
/holoscript:absorb + manual structure upload instead
- Highly confidential compounds — ChEMBL is public. Proprietary discovery work shouldn't emit compounds through this pipeline unless replacing with in-house equivalents.
- Speed-critical inference — this is a research/validation workflow, not a real-time decision tool. Scene generation takes 30-60 seconds even on warm caches.
Related
packages/plugins/alphafold-plugin/ — structure/binding-site/confidence traitsdocs/strategy/drug-discovery-flagship.md — why this is HoloScript's P0 verticaldocs/strategy/positioning-verifiable-digital-twin.md — category-creation narrative- W.GOLD.013 (Trust by Construction — Three Tiers of Simulation Trustworthiness)
- W.GOLD.015 (When Trust Is Free — 8 verticals that open)
- W.054 (Research → code latency <24h — the vault predicts shipping)
