abstain-dti
Calibrated abstention benchmark for drug–target interaction prediction, grounded in physical difficulty coordinates
2026년 현재 biomedical AI 에이전트는 폭발적으로 늘었습니다. Nature에 AI Scientist 논문 3편이 동시 게재됐고, Biomni는 Science에 실렸으며, Kosmos는 상업 스핀아웃으로 이관됐습니다. 그러나 현장이 지목하는 병목은 새 모델이 아니라 툴 연결·검증·벤치마킹입니다. Edison Scientific조차 도입 병목으로 신뢰와 검증을 지목합니다.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/pseudo-lab-abstain-dti — read its card at https://meshkore.com/agent/pseudo-lab-abstain-dti/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/pseudo-lab-abstain-dtiFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/pseudo-lab-abstain-dti/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own abstain-dti?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.