Deep learning of chemical reactions
This project aims to develop advanced deep learning frameworks for modeling organic and enzymatic reactions to enhance predictions of selectivity and enable sustainable synthesis.
Projectdetails
Introduction
The exploration of reactions is a central topic in chemistry. Compared to the success of machine learning for molecules, the modeling of reactions is lagging behind, especially for stereo- and regioselective reactions.
Need for New Approaches
Since current efforts toward sustainable synthesis, such as asymmetric organocatalysis or biocatalysis, rely on the accurate prediction of enantio- and regioselective reaction pathways, new modeling approaches are needed.
Project Aim
The proposed project aims to develop new, data-driven deep learning frameworks for modeling organic and enzymatic reactions, focusing on:
- Chemo-selectivity
- Regio-selectivity
- Stereoselectivity arising through intermolecular interactions with the reagent, solvent, or catalyst.
Detailed Objectives
In detail, we target:
- The rule-free, stereochemistry-aware modeling and subsequent experimental validation of asymmetric organocatalysis to identify new enantioselective transformations.
- The exploration of new biocatalytic synthesis pathways, including enzymatic cascades.
- The accurate prediction of activation energies via developing new deep learning approaches.
Methodology
We will expand molecular graph-convolutional neural networks and graph transformers to reactions in a rule-free manner. Additionally, we will introduce hidden three-dimensional representations to account for stereochemistry and intermolecular interactions. This will yield a versatile, open-source toolbox for reaction deep learning.
Significance of the Approach
This approach largely surpasses current methods, which rely on:
- Two-dimensional representations
- Reaction rules
- Three-dimensional input data
It offers the opportunity to model three-dimensional aspects and atom-mapping on-the-fly, for the first time, representing a significant breakthrough in this field.
Experimental Validation
The experimental validation campaign further allows for a direct application to the identification of new asymmetric organocatalytic transformations, as well as enzymatic cascades including cofactor recycling and side-product reduction, addressing the current need for more sustainable synthesis.
Financiële details & Tijdlijn
Financiële details
Subsidiebedrag | € 1.499.285 |
Totale projectbegroting | € 1.499.285 |
Tijdlijn
Startdatum | 1-10-2024 |
Einddatum | 30-9-2029 |
Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- TECHNISCHE UNIVERSITAET WIENpenvoerder
Land(en)
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