Toward fully autonomous closed-loop molecular discovery – A case study on JAK targets

ChemRxiv

Bridging AI and self-driving laboratories, we introduce the first fully-automated, closed-loop molecular discovery cycle, exemplified by the identification of novel JAK inhibitors. With minimal human intervention, we combined AI-driven molecular design and retrosynthesis with IBM’s synthesis automation system RoboRXN and Arctoris’ Ulysses platform for automated in-vitro screening. We performed two Design-Make-Test-Analyze (DMTA) cycles with a total of 36 synthesized compounds. In the first round, the generative model identified structural analogs of established JAK inhibitors despite being blinded from known JAK inhibitors. The second iteration yielded molecules with significantly improved pIC50 and ligand efficiency (p < 0.001) compared to the first round, underscoring the effectiveness of the closed-loop workflow.

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Toward fully autonomous closed-loop molecular discovery – A case study on JAK targets

Jannis Born 1, Carlo Baldassari 1, Doriela Grabocka 1, Antonio Cardinale 1, Oliver Schilter 1, Alessandro Castrogiovanni 1, Artem Leonov 1, Filip Skogh 1, Jeeven Singh 2, Yaoyao Xiong 2, John Evans 2, Thomas Fleming 2, Teodoro Laino 1, Matteo Manica 1

1) IBM Research - Zurich, Switzerland
2) Arctoris

ChemRxiv
https://chemrxiv.org/doi/10.26434/chemrxiv-2026-q7xdt

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