Agentic AI is everywhere, but most tools still fail on messy biomedical data with ambiguous terms, nested tables, and unstructured trial reports. Polly Xtract is built differently. It is an agentic AI system built to think like expert curators. It can convert unstructured trial reports into high-quality, analysis-ready datasets - including full trial schemas generated in minutes.
This matters across discovery and becomes critical in CNS programs where drugs must cross the blood–brain barrier. Teams need fast, comparable PK/PD signals to track competitors and know how competing drugs are performing in the market. This competitive intelligence often lies buried in vast volumes of clinical trial publications , scattered across protocols, CSRs, and appendices with inconsistent tables, mixed units, and variable terminology. Manual work takes weeks.
Xtract removes the bottleneck by automatically extracting and structuring clinical trial reports, capturing reported PK/PD values and labels, and organizing them into clean tables plus a complete trial schema (arms, inclusion/exclusion, endpoints, schedule of activities).
Join the session to see how this agentic workflow turns messy evidence into trustworthy PK/PD intelligence for BBB-penetrating drugs
Agentic AI is everywhere, but most tools still fail on messy biomedical data with ambiguous terms, nested tables, and unstructured trial reports. Polly Xtract is built differently. It is an agentic AI system built to think like expert curators. It can convert unstructured trial reports into high-quality, analysis-ready datasets - including full trial schemas generated in minutes.
This matters across discovery and becomes critical in CNS programs where drugs must cross the blood–brain barrier. Teams need fast, comparable PK/PD signals to track competitors and know how competing drugs are performing in the market. This competitive intelligence often lies buried in vast volumes of clinical trial publications , scattered across protocols, CSRs, and appendices with inconsistent tables, mixed units, and variable terminology. Manual work takes weeks.
Xtract removes the bottleneck by automatically extracting and structuring clinical trial reports, capturing reported PK/PD values and labels, and organizing them into clean tables plus a complete trial schema (arms, inclusion/exclusion, endpoints, schedule of activities).
Join the session to see how this agentic workflow turns messy evidence into trustworthy PK/PD intelligence for BBB-penetrating drugs
Scaling clinico-genomic data integration: Large pharmaceutical organizations working with external data providers used Polly to build interoperable clinico-genomic data products 6x faster.
Although purchased datasets are often labeled as "clean," they still lack interoperability—Polly's pipelines bridge this gap with robust integration and harmonization.
Information Retrieval: Drug safety monitoring teams used Polly's Knowledge Graph powered co-scientist to conversationally retrieve the right cohorts & assess drug response—cutting discovery time by 70%.
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If you’re working with complex biological data, you may be asking:
Can generative AI truly assist in scientific reasoning, not just data analysis?
What does it mean for hypothesis generation, literature review, or even designing experiments?
Could this accelerate—not replace—my discovery pipeline?
Whether you're skeptical, curious, or already experimenting with AI in your lab—this is a session designed to ground your understanding in evidence, not speculation.
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