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A Data-Driven Approach for the Optimization of Dynamic Kinetic Resolution Reactions

Project description

Predictive workflows for dynamic kinetic resolution

Supported by the Marie Skłodowska-Curie Actions programme, the DataDKR project aims to transform how scientists refine dynamic kinetic resolutions, a complex class of chemical transformations. Traditional methods often struggle with these reactions. DataDKR will combine experimental data with computational workflows to build more effective tools. Focusing on the synthesis of chiral phosphorus and sulphur molecules, the project seeks to develop statistical models that accurately predict reaction outcomes and identify the chemical drivers of material interactions. Ultimately, the project will provide a digital roadmap to streamline the handling of high-value compounds through data-driven insights. This advancement may bridge the gap between synthetic organic chemistry and modern information science and simplify intricate laboratory challenges.

Objective

The optimization of asymmetric catalytic transformations is a complex multivariate problem where numerous interdependent variables must be tuned simultaneously. While data-science approaches have successfully begun to guide reaction optimization, their application has largely been confined to relatively simple systems. The present DataDKR project aims to extend these methods to the more challenging yet synthetically useful class of dynamic kinetic resolutions (DKRs). Experimental and computational workflows will be combined to generate high-quality datasets, which will underpin the construction of predictive, statistically robust models capable of capturing intrinsic aspects of DKR processes across different substrates and background racemization strategies. Two synthetically relevant case studies—the enantioselective synthesis of chiral-at-phosphorus and chiral-at-sulphur compounds via DKR—will serve as benchmarks for model development and generalization. By identifying and encoding the unique features that govern the reactivity and selectivity of DKRs, the project will deliver a broadly applicable, data-driven framework for the rational optimization of these complex systems, advancing synthetic organic chemistry and data science in tandem.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global Fellowships

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Call for proposal

(opens in new window) HORIZON-MSCA-2025-PF

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Coordinator

UNIVERSIDAD AUTONOMA DE MADRID
Net EU contribution
€ 397 206,72
Address
CALLE EINSTEIN 3 CIUDAD UNIV CANTOBLANCO RECTORADO
28049 Madrid
Spain
Region
Comunidad de Madrid Comunidad de Madrid Madrid
Activity type
Higher or Secondary Education Establishments
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Total cost
No data

Partners (1)