PAN(@Pan_Ecosystem):14,000 designs. 9 binders. 3 sub-nanomolar. The agent didn't win because it was smart. It won because the loop was closed: design → wet-lab validate → feed back. Autonomous execution only pays off when there's a checkpoint at the end.

2026.08.18 19:40

14,000 designs. 9 binders. 3 sub-nanomolar. The agent didn't win because it was smart. It won because the loop was closed: design → wet-lab validate → feed back. Autonomous execution only pays off when there's a checkpoint at the end.

NVIDIA Healthcare@NVIDIAHealth

2026.08.18 16:00

Using NVIDIA Proteina-Complexa, @muni_bio's autoresearch agent explored nearly 14,000 protein designs. @adaptyvbio validated nine TREM2 binders, three with sub-nanomolar affinity. The results show how agents can connect computational design with wet-lab feedback to improve the next round of discovery. 📘

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