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Chemist-aligned retrosynthesis by ensembling diverse inductive bias models

Research AI for Chemistry

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TL;DR - This Nature paper presents a retrosynthesis approach that ensembles models with diverse inductive biases to better align predictions with chemists’ reasoning. Only the title and publication metadata are provided, so specific methods and results cannot be assessed.

  • The work focuses on retrosynthesis: inferring plausible precursor molecules and reaction pathways for a target compound.
  • It combines models designed around different assumptions or structural biases rather than relying on a single modeling approach.
  • “Chemist-aligned” indicates an emphasis on producing recommendations consistent with expert practice, but the provided content does not specify how alignment is measured.
  • The paper was published online in Nature on 21 September 2026.

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Chemist-aligned retrosynthesis by ensembling diverse inductive bias models

Nature Krzysztof Maziarz, Guoqing Liu, Felix Pultar, John Gardner, Tobias Gensch, Jean Helie, Hubert Misztela, Austin Tripp, Junren Li, Aleksei Kornev, Piotr Gaiński, Holger Hoefling, Mike Fortunato, Rishi Gupta, Andrew Baxter, Darren L. Poole, Jennifer M. Elward, Adrian Krzyzanowski, Peter Pogány, Stephen D. Pickett, Ian D. Wall, Christopher M. Bishop, Philip G. Humphreys, James A. Lumley, Mario P. Wiesenfeldt, Marwin H. S. Segler 2026-09-21 doi:10.1038/s41586-026-11160-9
Public signals OpenAlex citations 0
Providers: Hugging Face · N/A OpenAlex · Citations 0 Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-25 14:17:02.434928 UTC

TL;DR - This Nature paper presents a retrosynthesis approach that ensembles models with diverse inductive biases to better align predictions with chemists’ reasoning. Only the title and publication metadata are provided, so specific methods and results cannot be assessed.

  • The work focuses on retrosynthesis: inferring plausible precursor molecules and reaction pathways for a target compound.
  • It combines models designed around different assumptions or structural biases rather than relying on a single modeling approach.
  • “Chemist-aligned” indicates an emphasis on producing recommendations consistent with expert practice, but the provided content does not specify how alignment is measured.
  • The paper was published online in Nature on 21 September 2026.
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