Unlock the Power of Scientific AI | TetraScience

Scientists can now go from "I have an idea" to "I have a running app" in under an hour. See a live build at our August 13 Showcase.

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

Scientific outcome

29% reduction of experiments

Faster drug discovery through continuous feedback loops combining the virtual (cheminformatics) and real (ADME tests)

Prediction of:

Input

Scientific outcome

8x reduction of bioreactor runs per study

In silico prediction to suggest new media mixtures

Prediction of:

Input

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.

Make your scientific data work for AI.

Tetra OS turns scattered instrument and lab data into a foundation AI can actually use.

By transforming how our scientists access, analyze, and share research data, we're unlocking new levels of productivity and enabling AI-powered insights through a connected, online data environment. Beyond boosting productivity, we're leveraging data and agentic AI to accelerate innovation across our drug discovery engine.