# Coco and the Cancer-naughts

- **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:** [Lance Cadang](https://cerebralvalley.ai/u/lancecadang)
- **GitHub:** https://lancecadang-tech.github.io/ADC-engine-notebook/
- **Demo video:** https://youtu.be/mvuZYnm1F9Q?si=4lJpJVPKP1aVVq5H
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/163

The bottom line: An AI agent that reads why an approved cancer drug failed a tumor — then designs a new antibody–drug conjugate (ADC) engineered to dodge that exact resistance. Built end-to-end in Claude Science, grounded entirely in live data.

What I built:
One agent, five modules, each querying live databases — not memorized answers. Test case: TNBC that progressed on Dato-DXd (an approved ADC).

A · Resistance (ChEMBL, FDA, PubMed) → B · Target (Open Targets, GTEx, HPA) → C · Payload (PubChem, ChEMBL) → D · Structure (PDB, AlphaFold, Boltz-2 via NVIDIA BioNeMo) → E · Landscape (ClinicalTrials, bioRxiv)
Output: a fully-sourced dossier where every ID traces back to the query that produced it.

What I found
The failure is payload-class, not the molecule. Dato-DXd's TOP1-inhibitor warhead is cross-resisted by every TOP1i ADC — so the fix is a new mechanism, not a new antibody.
A resistance-orthogonal design: LIV-1 antigen (TROP2, the failed target, drops to rank 7) + MMAF payload (dodges both the TOP1i and the efflux resistance) + non-cleavable linker, DAR4.
Structure overturned the answer. Boltz-2 folded all three candidate targets and inverted the expression ranking: the top expression target (LIV-1) is the weakest structure (pLDDT 33); B7-H3 is the strongest (91). The real output is a two-axis map, not a single pick.
Why it matters
ADC resistance is a growing clinical reality with no systematic way to design the next drug against it. This engine makes resistance the design input, and — critically — lets one tool (structure) overturn what another (expression) would have asserted with false confidence. Reproducible, fully traceable, and it generalizes to any (indication, failed-drug) pair.

In-silico hypothesis, not a validated asset; key risks (LIV-1 foldability, payload tolerability) stated explicitly.

---

Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/163. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt