Predictive Polymer Analytics™
(PPA™)

Using secure retrieval-augmented generation (RAG) pipelines, PPA™ mines Likarda’s proprietary terabyte-scale database of polymer performance, encapsulation chemistries, and crosslinking strategies. The platform then recommends the most promising polymer backbones and formulation conditions. This data-driven approach means fewer trial-and-error experiments in the lab and faster paths to optimized results.

Traditional formulation development requires weeks or months of iterative testing to balance viability, stability, and bioavailability. With PPA™, timelines shrink from months to weeks, giving drug developers faster insights, fewer failed iterations, and higher confidence that they’re moving in the right direction. For clients advancing high-value therapies, speed to clinic is critical, PPA™ helps programs reach the next milestone sooner and with greater certainty.

Unlike generic AI tools, PPA™ is powered exclusively by Likarda’s curated, private dataset spanning thousands of use cases across modalities. It reflects a decade of hands-on encapsulation experience, turning hard-won lab results into predictive guidance for new programs.

Developed with Embedded Engineering ApS, PPA™ operates under strict confidentiality agreements using Claude-based models. All client embeddings and retrievals are performed in a secure environment, ensuring IP and proprietary data remain private and are never used to train public models.

Biotech and pharma developers advancing cell, gene, and large-molecule therapies who need a reliable, rapid, and secure way to move from discovery to clinic.