Scientific AI

Scientific AI by TetraScience is a purpose-built solution for life sciences organizations seeking to harness artificial intelligence and machine learning across the full pharmaceutical value chain. The Tetra Scientific Data and AI Platform addresses a fundamental challenge: every data silo in an organization — ELN, LIMS, instruments, and sensors — inhibits the data scale, liquidity, and engineering quality that AI requires to produce transformational outcomes. By replatforming scientific data and embedding deep domain expertise, TetraScience enables organizations to build AI data workflows that accelerate the speed and increase the efficiency of scientific decision-making.

TetraScience combines engineered data with a deep understanding of scientific outcomes, supported by a team of experts — called Sciborgs — who bring demonstrable expertise in scientific data, workflows, and use cases. This collaborative model allows internal AI and data science teams to rapidly prototype and productize Scientific AI outcomes through the Scientific AI Factory approach.

Core Pillars of the Scientific AI Approach

• Data replatforming: Provides access to large-scale, liquid, and high-quality data required for AI that is traditionally unavailable to data scientists due to fragmented legacy architectures.
• Scientific validation: Builds trust in AI predictions through scientific explanations and comprehensible process steps, removing opaque black-box models.
• Data engineering: Embeds domain knowledge into scientific data through science-enriched taxonomies and ontologies, ensuring data is fit for AI consumption.
• Continuous improvement: Improves models through ongoing re-validation, re-training, and human-in-the-loop scientist interaction to maintain accuracy and relevance over time.

Scientific AI Use Cases and Demonstrated Outcomes

• ADME-Tox studies in drug discovery: Inputs include plate reader data, assay data from ELN, well configuration, reagent information, molecular structure, and omics data. Use cases include ML-guided IC50 assay optimization and QSAR modeling to predict drug transporter and metabolizing enzyme interactions. Outcomes include a 29% reduction in experiments and faster drug discovery through continuous feedback loops combining cheminformatics and real ADME tests.
• Upstream bioprocess optimization: Inputs include instrument and sensor data, raw material characterization, and mechanistic understanding. The platform predicts viable cell density, glycosylation, titer, aggregation, cell viability, and charge variants. Outcomes include an 8x reduction in bioreactor runs per study and in silico prediction to suggest new media mixtures.
• Digital quality control: Inputs include instrument data, audit trails, and out-of-trend reports. The platform predicts deviations and supports AI-assisted root cause analysis. Outcomes include an 80% reduction in deviations, 90% faster investigation closure times, and a 200% boost in lab productivity.

AI Benefits Across the Pharma Value Chain

• Research: Improves target discovery by mining diverse datasets, increases the speed and accuracy of in silico molecule screening, explores a broader chemical space for de novo design, and uncovers new targets for known drugs by modeling drug and protein interactions.
• Development: Improves accuracy in predicting how drugs will behave in human subjects to eliminate unfavorable candidates earlier, and accelerates formulation development by rapidly probing large parameter spaces to identify optimal formulations.
• Manufacturing and QC: Continuously monitors production lines to anticipate process deviations, minimizes failures by tracking instrument wear patterns and identifying anomalies proactively, and enhances QC by preemptively flagging and addressing out-of-spec results.

The Tetra Scientific Data and AI Platform supports regulatory preparation through robust audit capabilities and unified access to instrument and CRO data, enabling automation and analytics at scale. TetraScience experts are available to help organizations assess their AI readiness and accelerate their data journey toward measurable scientific and operational outcomes.