Bayesian optimization identifies optimal conditions in far fewer experiments than one-variable-at-a-time testing or a fixed design of experiments, which is why it has become the preferred approach for chemistry R&D. Realizing that advantage depends on the platform used to run it, and platforms differ considerably in their capabilities, often in ways that become apparent only in practice. The ten criteria below outline what to evaluate before selecting a vendor.

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1. Confirm it shows how variables affect outcomes. Every Bayesian optimization platform should report how each variable affects your results, going beyond a ranking of which ones matter most to show the direction and magnitude of every effect and how the variables interact. This is what allows your team to build a genuine understanding of the chemistry underlying a formulation or process.

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2. Check that it flags data quality issues automatically. A capable platform detects missing values, outliers, and inconsistencies before they compromise a model. Automated warnings prevent unreliable results and reduce the need for manual data review.

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3. Confirm it handles high dimensionality, mixed parameter types, and custom featurization. Real projects are complex, so the platform must accommodate them without workarounds: up to 20 inputs and 20 outputs simultaneously, across both numerical and categorical variables. It should also support custom chemical features and encodings for complex chemistry, well beyond generic defaults.

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4. Ask for predictions at scale without new physical experiments. One of the clearest indicators of near-term ROI is the ability to submit a large file of input combinations and receive predictions for all of them, a capability sometimes called digital formulation or manual predictions. It replaces costly laboratory runs with computation, and the prediction accuracy should be clearly reported.

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5. Verify it is built for small datasets. Chemistry experiments are costly and time-consuming, so programs rarely begin with hundreds of data points. A platform built for chemistry should produce useful models and recommendations from as few as five experiments. Tools designed around large datasets tend to underperform in these conditions, or fail entirely.

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6. Require visualizations a bench chemist can read. Visualization matters more than many teams expect. The platform should show how variables are distributed and correlated, which ones drive the outputs and how they interact, the predicted surface across the input space, the tradeoff frontier when optimizing for multiple objectives, and how closely the model matches your data. A bench chemist with no machine learning background should be able to interpret these and act on them.

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7. Put bench chemists on the evaluation team. Include team members without a formal machine learning or data science background, particularly bench chemists. They provide the clearest test of whether the platform is usable across the organization, which is the purpose of adopting it.

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8. Test two to three applications at once. Evaluate two or three use cases in parallel during the proof of value. Testing more than one reveals how the platform performs across different types of problems and prevents a vendor from tailoring the demonstration to a single favorable scenario.

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9. Set quantifiable success criteria. Define measurable, numerical targets for each candidate platform before the evaluation begins. Concrete criteria remove the subjectivity that makes qualitative comparisons difficult to reconcile across evaluators.

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10. Vet the vendor's operational credibility. Finally, examine the vendor itself. SOC 2 and GDPR compliance reduce operational risk, and a credible track record should be verifiable through published, peer-reviewed work or documented customer results.

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Making the decision

No single feature determines the right choice. The platform worth adopting is the one that holds up across all ten criteria once the evaluation is complete, from the data it requires to the clarity of its recommendations. Run the evaluation on your own problems, with your own chemists, against the numerical targets you set in advance. That is what distinguishes a platform your team will adopt from one that stalls after onboarding.

How Sunthetics fits

Sunthetics is built for precisely these conditions: complex chemistry with limited data. It operates on as few as five experiments, allowing teams to reach a confident decision in fewer runs. It shows scientists which variables matter and why, making its recommendations straightforward to interpret. Because it requires no coding, the bench chemists conducting the work can operate it directly. Sunthetics is also SOC 2 certified and GDPR compliant, with results documented in peer-reviewed work.

To see how Sunthetics performs against your own criteria, explore the published case studies or book a call to learn more.

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Frequently asked questions

What should you look for in a machine learning platform for chemical process and formulation optimization?

Look for a platform that performs on small datasets, explains how variables affect your outcomes, handles high-dimensional and mixed-type problems, and comes from a credible vendor. The strongest platforms let a bench chemist interpret and act on the results without a machine learning background. The most reliable way to compare software is to evaluate these criteria on your own chemical process and formulation problems, against quantifiable success targets.

Can a machine learning platform optimize formulations and processes from small datasets?

Yes. Software built for chemistry R&D can produce useful models and experiment recommendations from as few as five data points. Tools designed around large datasets tend to underperform in the early stages of a program, when data is scarce, so small-dataset performance is worth confirming directly on your formulation and process problems.

What is the difference between Bayesian optimization and design of experiments (DOE)?

Design of experiments follows a fixed experimental plan set in advance, while Bayesian optimization updates its recommendations as new results arrive and directs each subsequent experiment toward the objective. This adaptive approach typically reaches optimal conditions in fewer experiments, particularly in high-dimensional or nonlinear systems where a fixed design becomes inefficient. The two can also be combined, with Bayesian optimization refining the models built from a DOE.

How does Bayesian optimization reduce the number of experiments in process development?

A platform purpose-built for chemistry can begin recommending experiments from as few as five, then choose each next run to add the most information toward the goal, which keeps the total number of runs low. The larger benefit in pharmaceutical and chemical process development is confidence: mapping the full design space to reach the true global optimum rather than settling on a local one, so the final decision is defensible.

What vendor credentials matter when choosing a Bayesian optimization platform?

Prioritize SOC 2 and GDPR compliance to reduce operational and data-security risk, alongside a verifiable track record. Published, peer-reviewed work or documented customer results are stronger evidence of capability than pilot claims alone.

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