AI Agents Match Humans Closely in Protein Binder Design Trial, Wet Lab Shows

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At a February 2026 one-day hackathon organized by muni, six autonomous AI agents including Claude Sonnet 4.6 designed protein binders targeting TREM2, a receptor relevant to neurological research. Adaptyv Bio tested 35 agent-designed binders in a wet lab, finding that 12 successfully bound TREM2, yielding a 34.3% hit rate. Human designers performed slightly better, achieving a 38.5% hit rate with 25 out of 65 binders binding successfully, though the gap was narrow. The campaign also produced high-affinity binders on both sides, with the strongest result coming from a human submission at 1.11 nM KD versus 3.64 nM KD for the top AI-designed binder. Experts caution that the aggregate AI result should not be attributed to any single model and that the findings reflect progress in AI-assisted biotech tooling rather than a proven shortcut to drug discovery.

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