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 benchmarks.
Assessing the Extrapolation Capability of Template-Free Retrosynthesis Models
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abstract
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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TempRe: Template generation for single and direct multi-step retrosynthesis
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 benchmarks.