About Deepflare

Deepflare provides an AI platform that predicts T-cell immunogenicity beyond simple binding affinity for antigen design. This platform accelerates discovery and de-risks drug pipelines by delivering superior candidate predictions with high accuracy. Users can design, iterate, and receive optimized candidates within 24 hours using integrated computational biology tools.

```xml <problem> 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. </problem> <solution> 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. </solution> <features> - *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. </features> <target_audience> Deepflare's primary customers are pharmaceutical companies and research institutions involved in the development of personalized cancer therapies and vaccines. </target_audience> ```

What does Deepflare do?

Where is Deepflare located?

Deepflare is based in Warsaw, Poland.

When was Deepflare founded?

Deepflare was founded in 2020.

How much funding has Deepflare raised?

Deepflare has raised $1.5M.

Location

Warsaw, Poland

Founded

2020

Funding

$1.5M

Employees

8 employees

Investors

Sunfish

Deepflare

Warsaw, PolandFounded 202081K+ followers9/10 TractionRelative Traction Score based on online presence metrics compared to companies in the same age group.

Updated 8 months ago

Funding

$1.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.

LSPKSPSS

Founders

Founder details are not available yet.

Product

Problem

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.

Solution

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.

Target Audience

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

Features

*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.