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Oscar, the Organic structures for catalysis repository, is a public EPFL database that gives artificial-intelligence researchers two related resources: a curated core of 4,000 experimentally derived organocatalysts reported by the EPFL team in 2022, plus molecular fragments and recombined structures that expand the chemical search space. That combination lets machine-learning models learn from known catalysts and lets generative algorithms propose combinations that have not yet been made. The proposals are hypotheses, not validated products: many fragment-derived and combinatorially enriched structures have not been synthesized or tested. Oscar therefore matters as a map and modelling foundation for data-driven catalyst discovery, not as a catalogue in which every entry has proven performance.
What is the Oscar organocatalyst database?
Oscar stands for Organic structures for catalysis repository. The EPFL project was created to organize a chemically diverse collection of organocatalyst structures in a form that computers can analyze. Organocatalysts are organic molecules used to accelerate chemical reactions; unlike metal catalysts, their useful behavior comes from the arrangement of non-metal atoms and functional groups in the molecule.
The database is designed for more than looking up individual compounds. Each structure can be represented through molecular descriptors—numerical features that describe aspects of its size, composition, shape or electronic character. Those descriptors give statistical and machine-learning methods inputs from which catalytic properties can be estimated, ranked or screened virtually.
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Clémence Corminboeuf, one of the project’s creators, described Oscar as an early step toward mapping organocatalyst space with broad chemical diversity. That framing is important: the resource is intended to make a large, structured region of chemistry searchable, while leaving experimental work to determine which predictions perform in a real reaction.
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How was Oscar assembled from known chemistry?
The EPFL team began with manual curation rather than an automatically generated list. Researchers collected catalyst structures from reviews, journal articles, books and commercial catalogues, then checked and standardized those structures for inclusion.
After building the curated set, the team treated the molecules as sources of reusable chemical motifs. Structures were fragmented into smaller building blocks and those fragments were recombined with different linkers. This process produces a much larger set of candidate molecules than the original literature alone contains. It also creates a deliberate separation between what chemistry has already demonstrated and what a computer has merely proposed.
Two evidence classes are mixed in one resource
| Entry type | How it was obtained | What it can support | What it does not prove |
|---|---|---|---|
| Experimentally derived organocatalysts | Structures collected from published and other manually curated sources | Training examples, descriptor analysis and searches grounded in reported chemistry | That the molecule will work in every reaction or under every condition |
| Fragments and recombined candidates | Motifs extracted from the curated structures and reassembled with linkers | Virtual screening, chemical-space exploration and generative-model proposals | That the candidate has been synthesized, stable, safe or catalytically active |
Keeping those classes separate is essential when preparing labels for a model. A measured catalyst can provide a target property when the underlying experiment is documented; a generated structure generally has no experimental label until a laboratory makes and tests it.
How large is the database?
The reported experimental core contains 4,000 organocatalysts, a figure attributed to the EPFL research team’s 2022 release. Around that core are several thousand molecular fragments and combinatorially enriched structures derived from the entries. The exact total depends on whether a count includes only the curated catalysts, the fragment library, every recombination, or duplicate and standardized forms.
It is therefore misleading to describe every row as one of 4,000 proven catalysts. The 4,000 figure applies to the experimentally derived core; the expanded portion is a design space containing structures that may never have appeared in a publication or laboratory notebook.
How can artificial intelligence use Oscar to discover catalysts?
Oscar supports a staged, data-driven workflow. A model can first learn relationships between molecular descriptors and measured catalytic behavior, then score untested structures. A generative method can search combinations of fragments for a desired profile, after which chemists select feasible candidates for synthesis and reaction testing.
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1. Define the reaction objective
Start with a specific transformation and measurable goal, such as higher conversion, selectivity, lower catalyst loading or improved tolerance to a condition. A model trained without a clearly defined reaction context may learn correlations that do not transfer to the intended experiment.
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2. Build a labelled training set
Use the experimentally derived entries for which reaction conditions and outcomes are available. Standardize structures, identify duplicates and record the provenance of each property. Treat missing measurements as missing data rather than silently assigning a zero or an assumed value.
3. Calculate or use molecular descriptors
Descriptors turn each structure into machine-readable variables. They allow a model to compare molecules that have not been tested in exactly the same way and can support similarity searches, regression, classification or uncertainty estimates.
4. Train and validate the model
Separate training and test examples by scaffold or structural family when possible, not only by random rows. A random split can place near-identical molecules in both sets and make performance look better than it will be on genuinely new chemistry. Validation should report the reaction, measurement and split conditions so a reader can judge what the score means.
5. Search the expanded chemical space
Once a model can rank known-like structures, screen fragments and recombined candidates. Genetic algorithms were identified by the EPFL team as one intended approach: an algorithm can treat fragment combinations as “individuals,” score them for the desired properties, retain promising combinations and generate new variants over successive rounds.
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Before ordering or synthesizing a top-ranked structure, check whether the proposed linker chemistry is feasible, whether the molecule is likely to be stable under the reaction conditions, and whether the required starting materials and purification route are realistic. A high model score is not a substitute for this feasibility review.
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7. Synthesize, test and feed back the results
Laboratory measurements determine whether a prediction is correct. New results can be added to the training set, allowing an iterative cycle in which each experiment improves the next round of ranking or generation. This closed loop is where a virtual candidate becomes evidence rather than a diagram on a screen.
Are Oscar’s generated molecules already synthesized and tested?
No. The database’s fragment-derived and combinatorially enriched structures are candidates created by recombination, and many have not been synthesized or tested. Robert Paton of Colorado State University and the Centre for Computer Assisted Synthesis highlighted the appeal of surveying structures that do not yet exist in the laboratory; that appeal is also the principal limitation.
Even an experimentally derived entry should not be treated as universally validated. A catalyst reported in one reaction may fail in another because solvent, temperature, substrate, concentration and competing pathways change its behavior. Oscar records a starting point for modelling and investigation, not a guarantee of performance.
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The 21 November 2022 report establishes the database’s purpose and the 4,000-catalyst experimental figure, but it does not establish current repository maintenance, an API, licensing terms, a version number or a complete metadata specification. Those operational details can change and should be checked on the repository page before a project begins.
- Provenance: determine whether a structure is literature-derived, a fragment, or a generated recombination.
- Property definitions: confirm exactly how activity, selectivity or another target was measured and whether values came from comparable reactions.
- Structure hygiene: inspect stereochemistry, protonation, salts, tautomers and duplicate representations before descriptor calculation.
- Dataset splits: test extrapolation to new scaffolds, not only interpolation among close analogues.
- Uncertainty: retain a confidence or applicability estimate so low-support predictions are flagged for review.
- Experimental plan: decide in advance how many candidates can be synthesized and what controls will establish a fair comparison.
How does Oscar compare with another chemistry dataset?
Oscar’s distinctive feature is the combination of a curated experimental core and an explicitly expanded fragment-and-recombination space. When comparing it with another dataset, the name alone is not enough; compare the evidence and metadata directly.
| Comparison question | What is established for Oscar | What must be checked for an alternative |
|---|---|---|
| Experimental or generated structures? | Both are present, and they are different evidence classes | Whether generated structures are separated from measured compounds |
| Breadth of catalyst classes | Designed to cover chemically diverse organocatalyst space; a class-by-class count is not stated in the 2022 report | Which classes, scaffolds and reaction families are represented |
| Descriptors and properties | Molecular descriptors are provided or intended to support property estimation; the full current schema is not stated | Which descriptors, labels, units and missing-value rules are supplied |
| Access and licensing | Described as a public database in the 2022 account; current terms are not stated | Repository availability, license, download format and API policy |
| Experimental validation of predictions | The project proposes future model and genetic-algorithm searches; commercial deployment is not reported | Whether published predictions have been synthesized and independently reproduced |
What are the main failure modes when using the database?
Confusing a proposal with a measurement
If generated candidates are mixed with measured catalysts, a supervised model may learn labels that were never observed. Keep a provenance column and use generated structures for ranking or exploration unless laboratory data later supplies a target value.
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Learning publication or catalogue bias
The curated set came from reviews, papers, books and commercial catalogues. Those sources reflect what was reported or made available, not every chemically possible catalyst. A model can therefore reproduce historical preferences while missing unusual but useful structures.
Overstating descriptor-based predictions
Descriptors summarize molecular features; they do not encode every solvent effect, transition state or experimental handling detail. Report the reaction context and uncertainty alongside any predicted score.
Assuming combinatorial validity
Joining two fragments that look compatible in a graph does not guarantee a viable synthesis or a stable product. Apply synthetic-planning and stability checks before treating a generated structure as an orderable compound.
Using stale access information
The public status and metadata described in 2022 may have changed. Confirm the current download, version and license from the maintained repository before redistributing data or building a production pipeline.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Where can researchers access Oscar?
The EPFL project described Oscar as a public database, but the 21 November 2022 account does not specify a current repository URL, API endpoint, release version or license. Researchers should locate the maintained EPFL repository through the project’s current publication or institutional pages and verify the terms shown there. Record the access date and release identifier in any analysis so that later users can reproduce the exact dataset.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsFor a first project, download the smallest documented release, preserve the original files, and create a local table that labels each structure by evidence class. That simple step prevents a generated candidate from being mistaken for an experimentally established catalyst when results are shared.
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What does Oscar make possible for catalyst discovery?
Oscar makes a practical bridge between known organocatalysis and algorithmic exploration. The 4,000-entry experimental core supplies chemically grounded examples, while fragments and recombinations give generative methods room to search beyond molecules already described in the literature. The bridge is useful only when provenance, reaction context and uncertainty remain visible.
The project’s stated direction is iterative: train models on available evidence, use genetic or other generative searches to propose promising combinations, then synthesize and test selected candidates. Until those experiments are done, a high-ranked molecule is a research hypothesis—not an established catalyst.
Frequently Asked Questions
Can I use Oscar to train a model without reaction-performance labels?
You can use the structures and descriptors for similarity searches, clustering or unsupervised representation learning, but those tasks do not predict catalytic activity by themselves. Performance prediction requires labels tied to defined reactions and measurement conditions; otherwise the output should be presented as chemical-space analysis rather than an activity forecast.
What software or hardware does Oscar require?
The 2022 account does not prescribe a programming language, package, model architecture or hardware specification. Requirements will depend on the current file format and the size of the generated subset you choose to screen, so check the maintained repository documentation before selecting an implementation.
How should a paper cite an Oscar-derived candidate?
Identify whether the structure came from the 4,000-entry experimental core or from fragment recombination, then cite the EPFL Oscar release and record its access date and version if available. For a newly generated molecule, also document the algorithm, fragments, linkers and filters that produced it so another team can reconstruct the proposal.
Can a genetic algorithm guarantee a better catalyst?
No. It can optimize the objective encoded in its scoring function, but that score may be biased, uncertain or disconnected from synthetic feasibility. Laboratory synthesis and controlled reaction testing are required before an apparent improvement is accepted as real.
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