CLASSY: cell like molecular assembly lines for programmable reactions

Over four years the multidisciplinary project known as CLASSY, funded under Horizon 2020 Future and Emerging Technologies and later aligned with EIC Pathfinder priorities, developed a body of work that explores how strategies used by living cells can be applied to synthetic chemical synthesis. The premise is straightforward. Living cells manage complex, multistep chemistry reliably and with minimal waste by organising reactions in compartments and using networks of catalysts. CLASSY set out to copy elements of that architecture at the lab scale with the goal of producing more efficient, adaptive and lower waste synthetic routes.

What the project set out to prove and why it matters

CLASSY was motivated by sustainability arguments that are now common in chemical research. Traditional industrial synthesis often requires multiple, separate reaction steps with intermediate purification. Each step can consume solvent, energy and reagents and produce waste. The project asked whether compartmentalisation and networked catalytic strategies borrowed from biology could reorganise synthesis so that sequences of transformations run in a regulated and productive way. The hope is that such architectures could reduce process mass intensity and energy use but that outcome depends on many engineering, scale and economic factors that the project did not aim to resolve fully.

Key technical advances reported

The consortium reported several technical advances across systems chemistry, catalysis and microfluidics. Partners emphasised progress rather than claims of ready industrial deployment. Notable themes were peptide based catalysis that is robust across conditions, the tandem use of peptide catalysts with enzymes in single pot transformations, studies of dynamic chemical networks, and the construction of a microfluidic microreactor platform to host compartmentalised reactions.

Beyond specific catalysts and devices CLASSY partners reported insights into the self organisation of multi component synthetic systems. These are fundamental science contributions that help map how far systems chemistry can approach cell like behaviours such as feedback, adaptation and hierarchical organisation.

Collaborations, dissemination and derivative projects

CLASSY was an explicitly multidisciplinary consortium. The project host and coordinator was the Biohybrid Materials and Systems Chemistry Group at Universidad Autónoma de Madrid led by Andrés de la Escosura. Partners included academic groups in Switzerland, the Netherlands, Austria and Israel and two companies providing microfluidic technology and project management. The project culminated in a final symposium on 15 March 2024 titled Catalysis in chemical networks and supramolecular assemblies. The online event attracted more than 50 participants from 42 organisations and served as a platform to present findings and to discuss future directions with external experts.

Some collaborations that grew from CLASSY have already led to new funded projects under Horizon Europe and other schemes. These include CORENET, an EIC Pathfinder Open effort coordinated by Andrés de la Escosura that started in April 2022 and aims to construct brain mimicking chemical computing devices made from networks of reactions. Other follow on actions include DarChemDN, a MSCA doctoral network, and MiniLife, an ERC Synergy project. These trajectories show how early stage projects can seed more ambitious programmes but they also underline that each further step is a new funding and technical challenge.

Automation and the push toward autonomous discovery

An important external development that complements CLASSY research is the large Dutch government investment of 97 million euro awarded to a project led by Wilhelm Huck to build a fully autonomous robot lab. This Robotlab initiative funded by the National Growth Fund aims to assemble hardware for automated experiments with closed loop design driven by artificial intelligence.

Proponents argue that automation plus AI can accelerate discovery and help tackle climate related problems by speeding up the development of greener materials and processes. Critics caution that automation amplifies existing biases in experimental design and that the collection of large experimental datasets does not by itself guarantee conceptual breakthroughs. Both perspectives matter for assessing the likely impact of the Robotlab and of AI enabled chemical research generally.

Challenges, limitations and the path from lab to industry

CLASSY faced expected challenges including disruptions from the pandemic and shifting research priorities over the project period. The science itself is complex. Demonstrating a concept at small scale does not automatically translate into reduced industrial waste or cost. Microfluidic and compartmentalised systems must be engineered for throughput, robustness and regulatory compliance before they can disrupt existing manufacturing processes. Enzyme and peptide combinations will need to prove stability, reuse and economic viability at scale.

Funding scouting and exploitation planning are active parts of the consortiums strategy. The presence of a company specialised in research funding services indicates an explicit route to chase further grants and to explore commercial pathways. That work is necessary but it does not guarantee commercial uptake.

Outlook and concluding assessment

CLASSY delivered meaningful scientific outputs and strengthened networks between systems chemistry, microfluidics and biocatalysis groups in Europe and beyond. It helped spawn new projects that probe chemical computation and aim to combine advanced automation with AI. The work sits at the intersection of promising scientific trends. At the same time the translation claims should be read as prospectus rather than proof. To move from demonstration to impact the field will need sustained engineering, reproducible scale up, robust automation that handles chemical complexity and evidence that new workflows improve environmental and economic metrics in production settings.

For policymakers and funders the CLASSY story is familiar. Early stage, multidisciplinary research builds knowledge and networks that can be leveraged. Long timelines and risk remain intrinsic to this kind of deep innovation. The investment in autonomous labs and in chemical computing reflects an appetite to accelerate the pipeline from discovery to application. How much acceleration results will be visible only after further validation and real world deployment.