AI-ready biophysical data

NanoTemper instruments help scientists generate structured, high-quality biophysical data for AI and machine learning workflows. The data can be used as model-training input, incorporated into analytical pipelines, and brought back to the laboratory to experimentally validate AI predictions.

NanoTemper data is AI-ready because it is high-quality, structured, machine-readable, and available for automated export. Instruments can provide raw data in JSON format, reducing the need for manual data conversion before results are used in machine learning pipelines or other analytical workflows.

The Prometheus Panta, Dianthus, and Monolith Omni support raw data export in JSON format. This enables teams to access structured results for analysis, data integration, model development, and experimental validation.

Yes. Prometheus Panta and Dianthus support batch raw data export through a command-line tool. This allows teams to retrieve data programmatically and integrate it into scripted or automated workflows without relying on manual export steps.

NanoTemper instruments provide options for automated data generation and workflow integration. Dianthus uHTS can be operated through gRPC for automated screening workflows, while Prometheus Panta Auto can load up to 1,536 samples using 384-well plates or can also be integrated into an existing automation workflow through gRPC.

Dianthus uHTS can measure 1,536 data points in less than 6 minutes, or more than 1,000 Kd values per hour, reaching nearly 100,000 data points in a single working day, through automated laboratory workflows via gRPC. This throughput can help teams build larger structured biophysical datasets for AI-supported drug discovery and validate model predictions at scale.

Prometheus Panta generates multi-parameter protein stability and colloidal stability data. In a single experiment, it can combine nanoDSF, Backreflection, dynamic light scattering, and static light scattering measurements for up to 48 samples. Results can include parameters such as melting temperature, onset temperature, aggregation temperature, particle sizing, and polydispersity index.

Multi-parameter measurements provide more context for each sample in a single experiment. Combining several measurements provides richer training or validation data for AI and machine learning models.

Protein thermal and colloidal stability data can support AI-driven formulation development. For example, Prometheus Panta can measure stability-related parameters that help researchers compare formulations, identify promising conditions, and provide experimental data for model- based formulation optimization.

Monolith Omni provides solution-based binding affinity measurements with minimal sample consumption. Its affinity data can be used to support binding-model development, compare compounds or candidates, and experimentally evaluate predictions generated by AI or other in silico tools.

Yes. NanoTemper measurements can serve as experimental ground truth for confirming, challenging, and refining predictions made by AI and other computational tools. This creates an iterative workflow in which models generate predictions, laboratory measurements test those predictions, and the resulting data can be used to improve future model development.

AI models are only as reliable as the data used to develop and evaluate them. Consistent, precise, reproducible, and sensitive biophysical measurements provide a stronger foundation for model training, analysis, and experimental validation.

Open and FAIR means designing data so results can be found, accessed, reused, and analyzed across tools, projects, and time. Structured data formats help teams connect biophysical results with analytical workflows, data platforms, and AI or machine learning pipelines.

Yes. Machine-readable JSON output, programmatic batch export, and structured data workflows make it easier for teams to access and reuse results across projects and analytical tools. This supports applications ranging from model training and analysis to experimental validation and workflow automation.

The appropriate solution depends on the scientific question:

• Prometheus Panta: Protein thermal and colloidal stability characterization, including multi-parameter stability data.
• Dianthus: Solution-based binding affinity measurements and screening workflows.
• Dianthus uHTS: Ultra-high-throughput binding screening for large-scale screening campaigns.
• Monolith Omni: Solution-based binding affinity measurements.

Each platform generates data suited to different stages of drug discovery, biopharmaceutical development, model development, and experimental validation.

NanoTemper instruments can support a lab-in-the-loop workflow:

1. Generate structured data from biophysical measurements.
2. Export the data for analysis or model development.
3. Use AI or machine learning models to generate predictions.
4. Test those predictions experimentally with biophysical measurements.
5. Feed the new results back into the workflow to guide the next round of experiments.

This approach connects computational predictions with laboratory evidence and helps scientists make faster, more confident decisions.