# DoctorDean

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Placement:** Finalist
- **Team:** [Dean Sherry](https://cerebralvalley.ai/u/DoctorDean)
- **GitHub:** https://github.com/DoctorDean/lazarus https://pypi.org/project/lazarus-bio/
- **Demo video:** https://youtu.be/zSC8miJpTtk
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/50

What I built. Lazarus is an agent that takes a dead research repository that is open, cited, and no longer runnable, and turns it back into a callable, containerized software component, from nothing but a GitHub URL. It reads the repo and the paper to write its own goal and a falsifiable success test, then runs a build → run → read-the-traceback → repair loop in a disposable Docker sandbox (CPU, remote, or GPU), pinning dependencies to the repo's commit era and fixing real source bugs along the way. Every revival emits the same "contract" in the form of a pinned container, a clean API/CLI, and a smoke test. This way revived tools from any language or era compose into pipelines, and the fixes go back upstream as pull requests. It's a real Python package on the Claude Agent SDK, published to PyPI (pip install lazarus-bio), with a docs site, a zero-setup Colab notebook, and CI.

What I found. I revived six abandoned methods spanning wildly different dead stacks: MaSIF, ScanNet, dMaSIF (GPU), fpocket (2010-era C), Basset (2016 Lua Torch7, genomics), and DiffDock (2023 diffusion docking), two of them from a bare URL with the agent writing its own plan. It didn't just make them run: it fixed a 15-year-old undefined-behaviour bug in fpocket's C, patched a CUDA path in dMaSIF that unlocked GPU execution the original forced onto CPU, and caught a silent soft-masking bug in Basset that a naive run would have shipped as a wrong result. It reproduced published benchmarks as a trust layer (MaSIF 0.82 vs 0.85; Basset AUROC 0.894 vs 0.895; DiffDock top-1 ≈0.375 vs the reported ~0.40), and when DiffDock's shipped example wouldn't honestly clear its own 2 Å success bar, the agent refused to fake a pass and reproduced the paper's aggregate accuracy instead. Composed together, the revived tools ran a live binder-triage pipeline on PD-L1 and correctly concluded "a flat protein–protein interface, an antibody target, not a small-molecule one."

Why it matters. Computational science has a reproducibility crisis that is really a software-decay crisis: a huge fraction of published methods stop running within a few years, and the cost of resurrecting them is days of dependency hell and niche debugging across compilers, drivers, and framework eras. This cost is high enough that people abandon and re-invent instead. Lazarus turns that days-long expert task into something anyone can trigger with a link and a pip install, and it does it honestly by reproducing the paper's numbers rather than just producing output that looks plausible. That's the difference between "the code ran" and "you can trust the result." For small, budget-constrained ML-for-biology teams, lowering that activation energy and keeping methods alive via upstream PRs is the whole point.

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/50. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt