What's missing from your AI stack
AI models are only as good as the data they're trained on. In most pharma and biotech organizations, that data is fragmented across hundreds of instruments, siloed in ELNs, and encoded in formats that weren't designed to be machine-readable.
The result: AI initiatives stall in data prep, models get built on incomplete training sets, and scientific knowledge resets with every program handoff.
Tetra OS addresses this at the architecture level — not as a workaround, but as the foundation.
The Rise of Sciborgs: Transforming How Science Gets Done
Introducing our new series: Sciborg™ Sessions. See how Sciborgs are fundamentally changing how science gets done.
Tune in live on LinkedIn:
February 27th | 11am ET
How Tetra OS works
Data replatforming
Provide access to large-scale, liquid, and high-quality data required for AI that traditionally is not available to data scientists.
Scientific validation
Trust your predictions with scientific explanations and comprehensible process steps, removing “black-box” models.
Data engineering
Embed domain knowledge into your scientific data through science-enriched taxonomies and ontologies.
Continuous improvement
Improve your models through continuous model re-validation, re-training, and scientist interaction (human-in-the-loop).
Build a high-throughput Scientific AI Factory
Generate AI-based scientific outcomes at a high pace with our Scientific AI Factory model, enabled by the Tetra Scientific Data and AI Platform.
Rapidly prototype Scientific AI outcomes through a collaboration with your internal AI and data science teams and Tetra Sciborgs.
Easily prioritize and productize final scientific AI outcomes.
Scientific AI use cases
Scientific AI is bringing unprecedented value to life sciences across the value chain. Here are three customer examples:
Use cases
Single parameter IC50 assay optimization—ML-guided concentration sampling reducing number of sampling points
Modeling of in-vitro ADME (QSAR) to predict interactions between samples and drug transporter/drug metabolizing enzyme
Input
Plate reader data
Assay data from ELN
Well configuration
Reagent information
Molecular structure
Omics data
Scientific outcome
29% reduction of experiments
Faster drug discovery through continuous feedback loops combining the virtual (cheminformatics) and real (ADME tests)
Prediction of:
Viable cell density
Glycosylation
Titer
Aggregation
Cell viability
Charge variants
Input
Instrument & sensor data
Raw material characterization
Mechanistic understanding
Scientific outcome
8x reduction of bioreactor runs per study
In silico prediction to suggest new media mixtures
Prediction of:
Prediction of deviations
AI-assisted root cause analysis
Input
Instrument data
Audit trails
Out of trend reports
Scientific outcome
80% reduced number of deviations
90% faster investigation closure times
200% boost in lab productivity
Use cases
Single parameter IC50 assay optimization—ML-guided concentration sampling reducing number of sampling points
Modeling of in-vitro ADME (QSAR) to predict interactions between samples and drug transporter/drug metabolizing enzyme
Input
Plate reader data
Assay data from ELN
Well configuration
Reagent information
Molecular structure
Omics data
Scientific outcome
29% reduction of experiments
Faster drug discovery through continuous feedback loops combining the virtual (cheminformatics) and real (ADME tests)
Prediction of:
Viable cell density
Glycosylation
Titer
Aggregation
Cell viability
Charge variants
Input
Instrument & sensor data
Raw material characterization
Mechanistic understanding
Scientific outcome
8x reduction of bioreactor runs per study
In silico prediction to suggest new media mixtures
Prediction of:
Prediction of deviations
AI-assisted root cause analysis
Input
Instrument data
Audit trails
Out of trend reports
Scientific outcome
80% reduced number of deviations
90% faster investigation closure times
200% boost in lab productivity
Benefit from AI in every stage of the pharma value chain
Research
Improve target discovery by mining diverse data sets. Increase the speed and accuracy of in silico molecule screening. Explore a broader chemical space to aid de novo design. Uncover new targets for known drugs by modeling drug and protein interactions.
Development
Improve accuracy of predicting how drugs will behave in human subjects, eliminating unfavorable candidates earlier in development. Accelerate formulation development by rapidly probing a large parameter space and identifying optimal formulations to test.
Manufacturing and QC
Continuously monitor production lines and anticipate process deviations. Minimize failures by tracking instrument wear patterns and identifying anomalies before they become problems. Enhance QC by preemptively flagging and addressing out-of-spec results.
