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Robot-mediated development of statistical models for mechanistic analysis amplification in synthetic organic reactions

Project description

Organic chemistry with machine learning

Organic chemistry often relies on trial-and-error methods, slowing progress in developing efficient synthetic strategies. Supported by the Marie Skłodowska-Curie Actions programme, the Cyb-org project aims to revolutionise this process by applying machine learning (ML) to optimise reaction protocols. A robotic platform will set up and sample reactions, collecting data on reaction rates, yields, and enantioselectivity. By developing statistical models, the project seeks to uncover mechanistic insights that can apply across various substrates and catalysts. This tool will not only enhance reaction efficiency but also identify outliers, potentially revealing novel reactivity. With potential applications in the pharmaceutical and fine chemical industries, Cyb-org promises to transform the way organic chemistry is optimised and scaled.

Objective

Cyb-org aims to apply well-established machine learning (ML) techniques to innovate mechanistic studies and optimization protocols in organic chemistry. Within this project, a robotic platform will be programmed to autonomously set up and sample reactions for kinetic analysis, extracting data on reaction rates, yields, and enantioselectivity. The primary goal of this proposal is to develop statistical models capable of generalizing mechanistic insights across a range of substrates or catalyst variables, focusing on the selected organic transformations. The new model will generate predicted outputs, which can subsequently be experimentally validated to achieve enhanced performance. Identifying outliers within this data will allow for the analysis of atypical reactions, potentially leading to the discovery of novel reactivity and the development of new, significant synthetic strategies. This advanced ML tool could be adopted by the pharmaceutical and fine chemical industries, enhancing automated systems for optimizing organic chemical transformations.

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Topic(s)

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

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

UNIVERSITAT DE GIRONA
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 322 164,36
Address
PLACA SANT DOMENEC 3
17004 GIRONA
Spain

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Region
Este Cataluña Girona
Activity type
Higher or Secondary Education Establishments
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Total cost

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Partners (2)

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