🧬 pack: drug-discovery-pack

Drive wet-lab synthesis without the Tanimoto-leakage scandal.

Predicting binding affinity / ADMET / toxicity? Generating molecules? Running GLP studies? Orchestrating instruments via SiLA2? GreatCTO auto-attaches drug-discovery-pack (trio: drug-discovery-ml + GLP + lab-automation) with scaffold/time/cluster split, applicability-domain, uncertainty calibration, ALCOA+ data integrity, CSA validation, and IQ/OQ/PQ qualification.

$ npx great-cto init All packs ↗
Auto-attach signals

Detected by CLI when:

rdkit · chembl · bindingdb · pdbbind · alphafold · rfdiffusion · LIMS · ELN · SiLA · GLP · GMP

The pack rides on top of your base archetype (web-service, ai-system, fintech, …) — it doesn't replace it. Auto-injects reviewer agents into the pipeline + opens human gates listed below.

Reviewer agents activated

3 specialists added to the pipeline.

01 · drug-discovery-ml-reviewer

Scaffold/time/cluster split + Tanimoto/sequence-identity threshold + AD bounds + UQ + retrospective validation + IP/patent FTO

02 · glp-glab-reviewer

21 CFR 58 + 21 CFR 211 + OECD GLP + ALCOA+ + EU GMP Annex 11 + FDA CSA (2024) + audit-trail SOP

03 · lab-automation-reviewer

SiLA2 + OPC-UA + LIMS chain-of-custody + IQ/OQ/PQ + protocol static analysis + scheduler safety

Human gates introduced

3 new gate types on top of gate:plan + gate:ship.

GateOwnerTrigger
gate:model-card-signoffML lead + clinical leadbefore wet-lab spend
gate:csv-validationindependent QA leadbefore GLP/GMP production
gate:iq-oq-pqengineering + QAper instrument qualification
Required artefacts before senior-dev claims tasks

10 concrete deliverables.

EVAL suite required

4 golden-set scenarios shipped as templates.

Each EVAL has ≥5 test cases, pass threshold, regression interpretation, cross-refs to TM + gates. Run via your existing test framework.

Regulatory surface covered

9 standards / regulations addressed.

21 CFR Part 58 (GLP) 21 CFR Part 211 (GMP) 21 CFR Part 11 OECD GLP EU GMP Annex 11 MHRA GxP Data Integrity (2018) FDA CSA guidance (2024) ISPE GAMP 5 (2nd ed.) USP <1058>
Real-world examples

30 companies in this space.

Absci
Generative AI for antibody discovery
publicUS
BenevolentAI
AI-augmented drug discovery
publicGB
Exscientia
AI-first drug design
publicGB
Ginkgo Bioworks
Programming cells at scale
publicUS
Recursion
AI-powered drug discovery at scale
publicUS
Schrödinger
Computational platform for life sciences
publicUS
Element AI / Borealis
Enterprise AI for bio (ServiceNow)
subsidiaryCA
Google DeepMind (Isomorphic)
AlphaFold + protein-structure AI
subsidiaryGB
Isomorphic Labs
AI for drug discovery (Alphabet)
subsidiaryGB
Benchling
R&D cloud for life sciences
growthUS
Valo Health
Human-centric AI drug discovery
series-dUS
Generate Biomedicines
Generative biology platform
series-cUS
Insitro
ML-driven drug discovery + development
series-cUS
Strateos
Cloud lab + robotic biology
series-cUS
Atomwise
AI for small-molecule drug discovery
series-bUS
Cradle Bio
Generative protein design platform
series-bNL
Iktos
AI for de novo drug design
series-bFR
Owkin
AI biotech for trials + drug discovery
series-bFR
Deep Origin
Compute platform for biotech
series-aUS
Abalone Bio
Antibody drugs others cannot develop
seedUS
Ångström AI
AI molecular simulations replicating lab results
seedUS
Future Fields
Recombinant proteins via insect bioreactors
seedCA
Lamin Labs
Open-source data infrastructure for biology
seedDE
Olio Labs
Combination therapeutics for difficult diseases
seedUS
om therapeutics
AI-driven manufacturing of medicines at scale
seedUS
ParcelBio
Next-generation mRNA medicines
seedUS
Persist AI
Long-lasting drug formulations 50% faster
seedUS
Radar Therapeutics
Controls cell and gene medicine
seedUS
Rosebud Biosciences
Grows micro-organs for drug discovery
seedUS
Talus Bio
Discovers drugs targeting the DNA regulome
seedUS

Listed companies operate in this space. Inclusion is based on publicly available product descriptions and does not imply endorsement of or by GreatCTO.

FAQ

Common questions about drug-discovery-pack.

When does drug-discovery-pack auto-attach?
When the CLI detects these signals in your repo: rdkit · chembl · bindingdb · pdbbind · alphafold · rfdiffusion · LIMS · ELN · SiLA · GLP · GMP. Override anytime by editing packs: in PROJECT.md.
What human gates does drug-discovery-pack introduce?
gate:model-card-signoff (ML lead + clinical lead), gate:csv-validation (independent QA lead), gate:iq-oq-pq (engineering + QA). These layer on top of the standard plan/ship gates.
What if my project doesn't match these signals exactly?
You can manually add the pack name to PROJECT.md or run /migrate to re-run detection with updated rules.
30 seconds

Drop GreatCTO into any repo — drug-discovery-pack attaches automatically.

$ npx great-cto init
no signup·runs locally·pay your own API