Substrate-adaptive reaction development enables modular access to functionalized activated olefins
- 1Chemistry Program, New York University Abu Dhabi (NYUAD), Saadiyat Island, United Arab Emirates (UAE
- 2Atinary Technologies, route de la Corniche, CH1066 Epalinges, Switzerland
Abstract
The decarboxylative olefination of aldehydes with malonic acid derivatives offers a mild and modular route to activated olefins, allowing the β-substituent, α-substituent and carbonyl-derived activating group to be varied independently. However, this modularity has not been realized within a unified framework: Existing methods remain fragmented across individual carbonyl classes and are dominated by αunsubstituted products, while access to α-heteroatom-substituted olefins, particularly α-halo derivatives, remains poorly developed. Moreover, these methods provide little guidance for adapting the reaction to new or challenging substrates. Consequently, despite its attractive features, decarboxylative olefination remains underutilized relative to established methods such as the Horner–Wadsworth–Emmons reaction. Here, we develop a unified, substrate-adaptive platform for the synthesis of functionalized activated olefins. Rather than optimizing a single model reaction and applying the resulting conditions across a fixed substrate scope, we used Bayesian optimization across representative substrate pairs to generate a compact, information-rich reaction-development dataset. This dataset was then used directly as prior knowledge for active transfer learning, enabling the identification of tailored, high-yielding conditions for new substrate combinations and complex targets with minimal additional experimentation. The resulting workflow provides access to α,β-unsaturated thioesters, esters and amides, substantially expands the scope of α-functionalized olefins and identifies effective conditions for substrates that were limitations of previous fixed-condition substrate scopes. More broadly, this study demonstrates that reactiondevelopment datasets can serve as transferable reaction knowledge, enabling efficient substrate-adaptive optimization and accelerating the maturation of emerging synthetic methods into practically general transformations.
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BibTeX
@misc{peng2026substrateadaptive,
title = {{Substrate-adaptive reaction development enables modular access to functionalized activated olefins}},
author = {Shaoting Peng and Woonkee S Jo and Daniel Pacheco Gutierrez and Chaemin Kim and Alan R Healy},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15001213/v3},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15001213/v3},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Substrate-adaptive reaction development enables modular access to functionalized activated olefins
AU - Shaoting Peng
AU - Woonkee S Jo
AU - Daniel Pacheco Gutierrez
AU - Chaemin Kim
AU - Alan R Healy
PY - 2026
DA - 2026/09/01
PB - ChemRxiv
DO - 10.26434/chemrxiv.15001213/v3
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15001213/v3
AB - The decarboxylative olefination of aldehydes with malonic acid derivatives offers a mild and modular route to activated olefins, allowing the β-substituent, α-substituent and carbonyl-derived activating group to be varied independently. However, this modularity has not been realized within a unified framework: Existing methods remain fragmented across individual carbonyl classes and are dominated by αunsubstituted products, while access to α-heteroatom-substituted olefins, particularly α-halo derivatives, remains poorly developed. Moreover, these methods provide little guidance for adapting the reaction to new or challenging substrates. Consequently, despite its attractive features, decarboxylative olefination remains underutilized relative to established methods such as the Horner–Wadsworth–Emmons reaction. Here, we develop a unified, substrate-adaptive platform for the synthesis of functionalized activated olefins. Rather than optimizing a single model reaction and applying the resulting conditions across a fixed substrate scope, we used Bayesian optimization across representative substrate pairs to generate a compact, information-rich reaction-development dataset. This dataset was then used directly as prior knowledge for active transfer learning, enabling the identification of tailored, high-yielding conditions for new substrate combinations and complex targets with minimal additional experimentation. The resulting workflow provides access to α,β-unsaturated thioesters, esters and amides, substantially expands the scope of α-functionalized olefins and identifies effective conditions for substrates that were limitations of previous fixed-condition substrate scopes. More broadly, this study demonstrates that reactiondevelopment datasets can serve as transferable reaction knowledge, enabling efficient substrate-adaptive optimization and accelerating the maturation of emerging synthetic methods into practically general transformations.
ER -