Project description
Advancing chemical reaction modelling
The accurate modelling of chemical reactions remains a significant challenge in the field of chemistry, particularly for stereo- and regioselective reactions. Current machine learning techniques have excelled in molecular modelling but have struggled to predict complex reaction pathways essential for sustainable synthesis, such as asymmetric organocatalysis and biocatalysis. In this context, the ERC-funded DeepRxn project aims to bridge this gap by developing innovative, data-driven deep learning frameworks that enhance the modelling of organic and enzymatic reactions. By focusing on chemo-, regio- and stereoselectivity, DeepRxn will employ molecular graph-convolutional neural networks and hidden three-dimensional representations, ultimately creating an open-source toolbox that accurately predicts activation energies and identifies new enantioselective transformations for sustainable synthesis.
Objective
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. 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. The proposed project aims toward developing new, data-driven deep learning frameworks for modeling organic and enzymatic reactions, focusing on chemo-, regio-, and stereoselectivity arising through intermolecular interactions with the reagent, solvent, or catalyst. 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, and the accurate prediction of activation energies via developing new deep learning approaches. We will expand molecular graph-convolutional neural networks and graph transformers to reactions in a rule-free manner, and introduce hidden three-dimensional representations to account for stereochemistry and intermolecular interactions, yielding a versatile, open-source toolbox for reaction deep learning. This approach largely surpasses current approaches, which rely on two-dimensional representations, reaction rules, or three-dimensional input data, in offering the opportunity to model three-dimensional aspects and atom-mapping on-the-fly, for the first time, representing a significant breakthrough in this field. Its 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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural scienceschemical sciencesorganic chemistryorganic reactions
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural scienceschemical sciencescatalysisbiocatalysis
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
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Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
1040 Wien
Austria