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. See: The European Science Vocabulary.
This project's classification has been human-validated.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been human-validated.
- natural sciences chemical sciences organic chemistry organic reactions
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
- natural sciences chemical sciences catalysis biocatalysis
- natural sciences computer and information sciences artificial intelligence computational intelligence
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2024-STG
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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.
1040 Wien
Austria
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.