Deepflare AI-Powered Immunogenicity Prediction Platform

Deepflare is an AI-powered in silico immunogenicity prediction platform designed for biotech and life sciences organisations working on vaccines, cancer immunotherapies, and viral therapies. Unlike conventional antigen design tools that rely on binding affinity as a proxy for immune response, Deepflare predicts true T-cell immunogenicity — delivering results with 12x greater accuracy than standard models such as netMHCpan 4.2. The platform is built for R&D teams that need bench-ready, computationally validated antigen candidates without the overhead of integrating disparate bioinformatics tools.

Deepflare's platform has been prospectively validated across multiple viral and cancer targets, demonstrating 12x higher Precision-Recall AUC in T-cell response prediction, 3x higher precision in a head-to-head in vivo cancer vaccine study, and a 100% confirmed expression rate in a prospective in vitro study on a difficult viral target.

Core Capabilities

• Predicts true T-cell immunogenicity rather than binding affinity alone, enabling more accurate modelling of immune responses — particularly critical in viral therapy applications.
• Supports de novo antigen design, taking users from a target protein to antigen candidates in seconds via an interactive demo environment.
• Allows iterative antigen design with custom feedback, enabling researchers to increase or decrease immunogenicity, improve solubility, and preserve or modify specific regions of interest.
• Delivers superior antigen candidates within 24 hours of applying design criteria and feedback.
• Integrates leading open-source computational biology tools — including RFdiffusion — alongside Deepflare's proprietary validated models into a single, seamless workflow.

Addressing Key Industry Challenges

• The Validation Crisis: Most antigen design models are benchmarked on biased datasets, producing inflated in silico metrics that fail when tested in vitro or in vivo. Deepflare is validated on prospective, real-world data to avoid this pitfall.
• The Wrong Target: Conventional tools conflate binding affinity with T-cell response. Deepflare models these two phenomena separately, which is especially important for viral therapy programmes.
• The Usability Barrier: The volume of new bioinformatics and machine learning tools makes it impractical for innovative biotechs to evaluate and implement them independently. Deepflare consolidates the best open-source tools with its own proprietary models into one accessible platform.

Workflow Overview

1. Input a target protein into the platform to initiate antigen design.
2. Apply custom design criteria — such as modifying immunogenicity levels, adjusting solubility, or preserving specific sequence regions.
3. Receive optimised, bench-ready antigen candidates within 24 hours.
4. Iterate on candidates using the platform's integrated computational biology toolkit.

Deployment and Business Model

• A free tier is available online, providing access to basic platform features for initial evaluation.
• Premium features — including region modification and immunogenicity optimisation — are available via a SaaS subscription model.
• Enterprise customers can access bespoke data training, enhanced governance features, and commercial exclusivity on specific targets, in addition to full platform capabilities.

Deepflare is suitable for innovative biotech companies and research organisations seeking a validated, integrated computational platform to de-risk antigen pipelines and accelerate immunotherapy and vaccine discovery programmes.