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bioRxiv · 10.64898/2026.09.08.749228

Agent-driven Model Development for RNA 3D Structure Prediction

Majewski, M.·Malo, L.·Montero-Blay, A.·Marengo, M.·Gkeka, P.·Minoux, H.
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Abstract

Large language model (LLM) agents have shown promise in driving scientific discovery, but their effectiveness in complex, real-world biological problems remains underexplored. We ask whether a general-purpose LLM agent can drive semi-autonomous development of a model for a genuinely hard biological problem, RNA 3D structure prediction. We designed a development loop where, under a fixed budget and with human supervision, the agent iteratively proposed, implemented, trained, and evaluated model changes. Over 297 iterations, the model evolved from a randomly-initialised baseline to QuickFold, an 8.9M-parameter folding trunk that matches the strongest open-source baselines (RhoFold+, NuFold) on lDDT and TM-score within noise on a held-out test set at a fraction of their inference cost. We frame this less as a new predictor than as a case study in feedback-driven, agent-led model development.

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Majewski, M., Malo, L., Montero-Blay, A., Marengo, M., Gkeka, P., Minoux, H.. 2026-09-08. Agent-driven Model Development for RNA 3D Structure Prediction. https://doi.org/10.64898/2026.09.08.749228

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