Training Alliance for Computational Systems chemistry

Project Information

TACsy

Grant agreement ID: 101072930

DOI

10.3030/101072930

EC signature date 6 July 2022

Start date 1 March 2023

End date 28 February 2027

Funded under

• Marie Skłodowska-Curie Actions (MSCA)

Total cost

No data

EU contribution

€ 3 872 336,40

Coordinated by

SYDDANSK UNIVERSITET
Denmark

Periodic Reporting for period 1 - TACsy (Training Alliance for Computational Systems chemistry)

Reporting period: 2023-03-01 to 2025-02-28

Summary of the context and overall objectives of the project

Many key questions and challenges in research, industry, and society involve large and complex Chemical Reaction Networks (CRNs). Examples of such challenges include: understanding the regulation of metabolic networks in humans; planning and optimizing chemical synthesis in industry and research labs; modeling the fragmentation of molecular ions inside mass spectrometers; developing personalized medicine; probing hypotheses on the origins of life; and monitoring environmental pollution in air, water and soil.
Much of classical chemistry focuses on a small set of molecules and reactions at a time. Systems chemistry is an emerging field that addresses the need to study larger CRNs, such as those in the challenges listed above. Due to the size and combinatorial complexity of these systems, it is unfeasible to manually analyze their properties and explore their design space. The field is therefore in strong need of new computational methods to assist in analysis, modeling, and design.
Members of the consortium behind the Training Alliance for Computational Systems Chemistry (TACsy) are constructing ground-breaking new computational methods for analyzing large CRNs. In TACsy, we will develop and unfold the potential of these methods and we will train a new generation of 14 excellent Doctoral Candidates (DCs) capable of evolving and applying these methods in research and industry.
In project TACsy, we will develop ground-breaking new computational methods for analyzing such networks of chemical reactions and we will train a new generation of excellent and innovative early stage researchers (DCs) capable of evolving and applying these methods in research and industry. Combined, these efforts carry very strong potential for impact on the grand challenges mentioned above, on the EU commission priority on jobs, growth, investment, and competitiveness, and on the well-being of EU citizens.
The research methodology of TACsy arises from the novel application of formalisms, algorithms, and computational methods from computer science to questions in systems chemistry. The first steps demonstrating the strong capabilities of this approach have recently been made. In TACsy, the DCs will vastly expand these methods and their formal foundations, they will create efficient algorithms and implementations of them, and they will use these implementations for research in complex chemical systems in three flagship application areas.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The TACsy project is advanced Computational Systems Chemistry (CSC) by developing a novel computational framework based on Double Pushout (DPO) graph transformation, combining atomistic accuracy and computational efficiency for analyzing large and complex Chemical Reaction Networks (CRNs). Key achievements include creating tools for automating multi-enzyme cascade design, modeling lipid metabolic networks, and optimizing CO2 recycling pathways. The project integrated quantum chemistry, machine learning, and graph theory to enable applications in metabolic engineering, environmental chemistry, and prebiotic studies. Collaborations with industry partners (BASF SE, Thermo Fisher, Fluigent) further expand its impact, delivering computational methods and tools that streamline experimental workflows, enhance industrial processes, and open new research avenues. These advancements position CSC as a transformative methodology for addressing challenges in chemistry and life sciences.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The TACsy project has established Computational Systems Chemistry (CSC) as a transformative methodology for modeling and analyzing complex Chemical Reaction Networks (CRNs). Key results include a highly efficient DPO-based framework, tools for sustainable chemical processes, machine learning-driven reaction discovery, and advanced stochastic simulations. These achievements support applications in industrial optimization, metabolic engineering, and prebiotic chemistry, with significant potential for scientific innovation, economic growth, and educational impact. To ensure further uptake, key steps include expanding research to new domains, securing IPR and commercialization pathways, developing market-ready tools, aligning with regulatory standards, and fostering global partnerships for broader adoption and impact.