CassidyBio offers an AI‑driven genomic foundation model that predicts optimal guide RNAs, editors, and delivery contexts for therapeutic gene‑editing projects. By training on proprietary high‑throughput wet‑lab and sequencing data, the platform reduces experimental cycles from months or years to weeks, accelerating the design of clinical‑grade genome‑editing therapies for biotech, pharma, and academic researchers.
Funding
Funding not disclosed
Founders
Product
Problem
Designing therapeutic-grade gene editing solutions currently relies on extensive trial-and-error experiments, leading to long development timelines and high costs. This slows the delivery of potentially life‑saving genome editing therapies to patients.
Solution
CassidyBio provides an intelligence layer for gene editing by offering a genomic foundation model that predicts optimal guides, editors, and delivery contexts for therapeutic applications. The platform is trained on proprietary high‑throughput wet‑lab assays and next‑generation sequencing data collected under therapeutic conditions, enabling rapid, systematic design of genome editing systems. By converting the genome into a language that the model can interpret, researchers can move from months or years of experimental iteration to weeks of predictive design, accelerating the path to clinical candidates.
Target Audience
Primary customers are biotech and pharmaceutical companies, as well as academic research groups, that are developing in vivo gene editing therapies and need accelerated, reliable design of editing components.
Features
- Proprietary wet‑lab dataset generation pipeline that captures high‑throughput assay results and NGS validation in therapeutic settings
- Large‑scale genomic foundation model that predicts guide RNA efficacy, editor selection, and delivery modality for specific targets
- Integrated AI‑driven design workflow that reduces experimental cycles and shortens development timelines
- Validation framework linking model predictions to experimental outcomes for continuous model improvement
- Scalable cloud‑based platform that supports collaborative design across multidisciplinary teams