The development of personalized cancer therapies is hindered by the difficulty in identifying effective neoantigens that will elicit a strong immune response. Traditional methods are often time-consuming, expensive, and may not accurately predict immunogenicity.

Deepflare offers an *in silico* antigen discovery platform that leverages machine learning to analyze genomic data and identify neoantigens for personalized cancer therapies and vaccine development. The platform uses advanced computational algorithms to predict antigenicity and immunogenicity, optimizing the selection of biotherapeutic candidates. By integrating computational modeling with laboratory automation systems, Deepflare streamlines experimental processes and accelerates data analysis, enabling faster and more efficient drug development. The technology aims to improve cancer treatment outcomes by providing accurate and efficient neoantigen discovery solutions.

*In silico* neoantigen discovery using advanced computational algorithms and machine learning.

Immunogenicity modeling service to assess the immunogenic potential of biotherapeutic candidates.

Lab-in-the-loop solutions integrating laboratory automation systems with computational modeling.

Predictive algorithms to evaluate the interaction between the drug and the immune system.

Streamlined experimental processes and optimized experimental design.

Accelerated data analysis pipeline.

Deepflare's primary customers are pharmaceutical companies and research institutions involved in the development of personalized cancer therapies and vaccines.