# Rare Events

- **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
- **Team:** [Michael Kim](https://cerebralvalley.ai/u/mckim)
- **GitHub:** https://myungchulkim.com/research/claude_hackathon.html
- **Demo video:** https://myungchulkim.com/research/claude_hackathon.html#demo-video
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/167

Protein conformational rates are hard to compute: the transitions are rare, so brute-force molecular dynamics almost never sees them. Importance sampling accelerates them by biasing the dynamics and reweighting back to the true dynamics, but the path weights degenerate -- their variance grows exponentially and a handful of paths dominate, making the rate estimate unreliable. We asked which weight-control (resampling) scheme fixes this for a real peptide, and answered it as an agentic method search: Claude Code proposed each estimator variant, implemented it, ran it on the C5 // C7eq isomerization of alanine dipeptide, and read back the weight statistics and effective sample size against an exact brute-force rate -- then iterated. Comparing sequential importance sampling (SMC), sequential importance resampling (SIR), and a branching resampled random walk (RRS), only RRS converged in both directions, recovering the true rate within 3–6% where the naive committor estimate is up to 6× wrong. An agent discovered the variance-reduction scheme, not just applied one.

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/167. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt