Regen Workbench

Local Docker lab that covers most of the ChatGPT “Scientific Research” plugins and DeepMind Science Skills without putting those vendors in the loop for every lookup.

It is meant to be the computer that Codex / Grok Build / Antigravity drive, instead of their small cloud sandboxes.

Research direction: prioritize exploratory molecular modeling, simulation, and direct gene-expression and compound-data analysis for aging and regeneration. See Research philosophy and priorities for the owner's ambitions and approach to scrutinizing evidence and bias.

Interactive research desk

The research desk adds editable hypothesis blueprints, literature/registry/web searches, explicitly tagged anecdote and vendor-claim intake, PubChem structural neighbors, small-molecule analog enumeration, property comparisons, and reproducible RDKit conformer runs. Existing API keys stay in the gitignored .env; the browser receives configuration flags and research results, never credentials.

docker compose -f compose.research.yaml up -d --build

Open http://127.0.0.1:8092. This lightweight service does not require the full scientific/folding image. Runs and saved observations stay under data/research-desk/; fetch receipts remain under data/provenance/. The default priorities are somatic mutation repair, engineered tissues and nanomedicine, followed by organs, senescence and structural restoration.

Read the API and chemistry campaign report: 57 runs, 173 provider records before deduplication, 236 verified artifact hashes, four compared dipeptides, six structural hypotheses and 30 converged conformers. Run counts are not independent studies or efficacy evidence.

New local activities: compare expression datasets and explore compound conformers, with runnable examples, sensitivity diagnostics, 3D SDF outputs, and reproducible input/parameter records. Use regen expression-contrast --help, regen compound-screen --help, and regen pipeline --help (chained contrast -> senescence scoring -> benchmark evaluation with cryptographic provenance wiring).

What you get after bootstrap

Layer	Contents
Workbench image	Python 3.11, JupyterLab, RDKit, Open Babel, Open-source PyMOL, Biopython, Scanpy, Nextflow, MAFFT/MUSCLE/ClustalO, BLAST, MMseqs2, Foldseek, HMMER, FastQC, MultiQC, seqkit, samtools, minimap2, AutoDock Vina, fair-esm, regen CLI
Sibling image (optional)	Official ColabFold CUDA image, pulled on --gpu
Volumes	projects/ (git repos), data/ (fetched files), cache/ (weights)
GPU	--gpus all + 16 GB /dev/shm
MCP server	Allowlisted research tools exposed to MCP-capable coding agents

What is not baked in: AlphaFold 3, full AF2 genetic databases, RFdiffusion weights, Schrödinger, wet-lab robots. See MANUAL_ACCOUNTS.md.

Laptop prerequisites

• Docker Engine + Compose v2
• NVIDIA driver + NVIDIA Container Toolkit for GPU workloads
• Enough disk for images + caches (budget 40–80 GB the first week)

Start

chmod +x scripts/*.sh tools/regen tools/regen.py tools/regen_mcp.py
./scripts/host-setup.sh
./scripts/bootstrap.sh          # start workbench, Jupyter, and MCP service

On Windows (PowerShell, no Git Bash/WSL needed):

.\scripts\Host-Setup.ps1
.\scripts\Bootstrap.ps1          # start workbench, Jupyter, and MCP service

For GPU workloads, check GPU support and start with the GPU override:

./scripts/host-setup.sh --gpu
./scripts/bootstrap.sh --gpu    # also pull ColabFold

Then:

docker compose exec workbench bash
regen doctor
regen uniprot P04637
regen afdb P04637
regen pubmed "AK2 splice variant iPSC" --retmax 8

The default workbench starts without a GPU reservation. Use --gpu when running GPU workloads; to enable GPU access on an existing installation, run ./scripts/bootstrap.sh --gpu again. The MCP service remains CPU-only.

Jupyter: http://127.0.0.1:8888 (loopback only; keep Jupyter's token enabled).

MCP tools for coding agents

The startup script runs a dedicated mcp service without published ports, host credentials, or a Docker socket. Configure Codex, Cline, or a local Antigravity client that supports custom stdio servers to launch the allowlisted stdio server through the laptop's Docker Compose CLI. See MCP_SETUP.md for client-specific setup.

Tools cover PubMed, EuropePMC, OpenAlex, UniProt, InterPro, Ensembl, AFDB/PDB fetches, STRING, ChEMBL, PubChem, local sequence alignment, RDKit descriptors, batch compound conformers, expression contrasts, PyMOL rendering, fold routing, and diagnostics. The server also exposes read-only MCP resources: the tools.yaml capability map and the provenance receipt log, so agents can inspect what the workbench supports and what data has already been fetched. The server has no arbitrary shell tool and does not run folding models. Fetch/render calls write provenance under data/provenance/.

How to use this with paid agents

1. Keep this container running.
2. Open the repo you want improved inside /projects.
3. Configure the selected agent with the MCP instructions in MCP_SETUP.md.
4. Paste agent/AGENT_BRIEFING.md as standing project guidance.
5. Review diffs and data/provenance/; keep one agent on a repo at a time.

Your 64 GB / 16 GB machine will beat a typical vendor sandbox on Scanpy, RDKit, MSA, and modest folds. Their cloud box still wins at unattended multi-hour jobs if you close the laptop — so do not start a 4-hour Boltz job on battery.

Layout

regen-workbench/
  docker-compose.yml
  .dockerignore           # excludes secrets/data/projects from image context
  docker/workbench.Dockerfile
  tools/regen.py          # plugin stand-in CLI
  tools/regen_mcp.py      # allowlisted MCP stdio server (tools + resources)
  tests/test_regen_mcp.py # protocol/security tests
  MCP_SETUP.md            # Codex, Cline, Antigravity client setup
  config/tools.yaml       # capability map
  scripts/host-setup.sh   # Linux/macOS/Git Bash
  scripts/bootstrap.sh    # Linux/macOS/Git Bash
  scripts/Host-Setup.ps1  # PowerShell (Windows)
  scripts/Bootstrap.ps1   # PowerShell (Windows)
  agent/AGENT_BRIEFING.md
  agent/PROJECT_SEEDS.md
  MANUAL_ACCOUNTS.md
  projects/               # your GitHub checkouts
  data/                   # fetched biology artifacts
  cache/                  # model weights, ColabFold cache

Development checks

The CLI and MCP regression tests use the Python standard library, mock remote APIs, and do not require database credentials, a GPU, or the full scientific image. From this repository in the shared workspace, run:

docker compose -f ../compose.yaml run --rm --no-deps dev python3 -B -m unittest discover -s regen-workbench/tests -v

For a standalone checkout with Python 3.11 available, run python -B -m unittest discover -s tests -v from the repository root.

The MCP server returns protocol errors for malformed resource requests and unreadable resource files, keeping the session available for later requests. Oversized input lines are discarded in bounded chunks. Individual resource files are read only up to the output limit before a truncation marker is added.

Frozen cohort check

studies/frozen_cohort/PROTOCOL.md is the estimand. The runner wires regen expression-contrast, an explicit Welch/BH view, a GiWi window fixture, and a group-aware morphology holdout. Age is aliased with donor on purpose. A shared batch id is refused instead of scored. atlas_link.json carries the phenotype manifest hash for a later compound-atlas citation. The hash is not an activity label.

PYTHONPATH=tools python3 studies/frozen_cohort/run_study.py --out artifacts/frozen-cohort

The output directory must be new. The cohort is synthetic.

License

Scripts in this folder are CC0. Third-party tools keep their own licenses (RDKit BSD, PyMOL BSD-like open-source build, ColabFold / AF2 weights Apache + DeepMind terms, Boltz separate, NCBI data use policies). Read those before you publish a paper off this stack.