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Assessing the Extrapolation Capability of Template-Free Retrosynthesis Models

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arxiv 2403.03960 v1 pith:EJ6WV5IR submitted 2024-02-29 physics.chem-ph cs.LG

classification physics.chem-phcs.LG
keywords modelstemplate-freecapabilityreactionsnoveldespiteextrapolationreaction
verification ladder T0 review T1 audit T2 compute T3 formal
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Despite the acknowledged capability of template-free models in exploring unseen reaction spaces compared to template-based models for retrosynthesis prediction, their ability to venture beyond established boundaries remains relatively uncharted. In this study, we empirically assess the extrapolation capability of state-of-the-art template-free models by meticulously assembling an extensive set of out-of-distribution (OOD) reactions. Our findings demonstrate that while template-free models exhibit potential in predicting precursors with novel synthesis rules, their top-10 exact-match accuracy in OOD reactions is strikingly modest (< 1%). Furthermore, despite the capability of generating novel reactions, our investigation highlights a recurring issue where more than half of the novel reactions predicted by template-free models are chemically implausible. Consequently, we advocate for the future development of template-free models that integrate considerations of chemical feasibility when navigating unexplored regions of reaction space.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TempRe: Template generation for single and direct multi-step retrosynthesis

    cs.LG 2025-07 conditional novelty 6.0 of 10

    TempRe shows that autoregressively generating reaction templates, and even whole synthesis routes as template sequences, beats template classification and SMILES generation on single-step and multi-step retrosynthesis...

  2. Challenging reaction prediction models to generalize to novel chemistry

    cs.LG 2025-01 conditional novelty 6.0 of 10

    A Transformer reaction predictor evaluated on document-, author-, time-, and reaction-class splits reveals that in-distribution benchmarks overstate real-world accuracy and that models only partially extrapolate to un...

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