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TUE, 22 SEPT · 78 ITEMS
"Chemist-aligned retrosynthesis via ensembling diverse inductive bias models" — Nature (current) · Science & Medicine
This is a machine-learning chemistry paper, likely in Nature, presenting a method that combines multiple models with different inductive biases — such as template-based, graph-based, and sequence-based approaches — into an . The goal is to improve the accuracy and robustness of planning how to synthesize a target molecule by breaking it down into simpler precursors, with the ensemble approach mitigating individual model weaknesses.
The claim is solid as a primary-source report from the authors, but the specific details of the method and results are inferred from the title and note; the actual experimental outcomes and comparisons are not available from the provided text.
The item is presented as current, with no recirculation year indicated, so it reflects newly published research.