Deepflare

Deepflare is an AI-powered in silico immunogenicity prediction platform designed to transform antigen design for vaccines and immunotherapies. Unlike conventional tools that rely on binding affinity as a proxy for immune response, Deepflare predicts true T-cell immunogenicity, enabling biotech and pharmaceutical teams to de-risk their pipelines, accelerate discovery, and generate superior antigen candidates with significantly greater accuracy.

The platform has been validated across multiple viral and cancer targets in prospective in vitro and in vivo studies, consistently outperforming state-of-the-art models when applied to real-world R&D challenges. Deepflare's approach addresses a well-documented validation crisis in the field, where widely used tools such as netMHCpan 4.2 are benchmarked on biased data, producing inflated in silico metrics that fail to translate to wet-lab results.

Validated Performance Benchmarks

• 12x higher Precision-Recall AUC in T-cell response prediction compared to leading standard models
• 3x higher precision demonstrated in a prospective, head-to-head, in vivo cancer vaccine study
• 100% confirmed expression rate validated in a prospective in vitro study on a difficult viral target
• Platform validation has been confirmed in a prospective in vivo study, establishing real-world credibility beyond computational benchmarks

Core Platform Capabilities

• De novo antigen design: Go from a target protein to antigen candidates in seconds using an interactive, AI-driven workflow
• Custom design criteria: Apply iterative feedback to increase or decrease immunogenicity, improve solubility, and preserve or modify specific regions of interest
• Superior candidate delivery: Design, iterate, and receive optimized antigen candidates within 24 hours
• True T-cell immunogenicity modeling: Separates binding affinity prediction from T-cell response modeling, which is especially critical for viral therapy applications
• Seamless computational workflow: Integrates leading open-source computational biology tools such as RFdiffusion alongside Deepflare's proprietary validated models into a single unified platform

Key Problems Addressed

• The Validation Crisis: Most antigen design models are trained and benchmarked on biased datasets, creating a cycle of inflated metrics that fail in vitro and in vivo; Deepflare is built and validated against prospective real-world data
• The Wrong Target: Conventional tools conflate binding affinity with T-cell response; Deepflare models these as distinct biological phenomena, improving predictive accuracy particularly in viral therapy contexts
• The Usability Barrier: The proliferation of bioinformatics and machine learning tools makes it impractical for innovative biotechs to evaluate and implement them all; Deepflare consolidates the best open-source tools with its own proprietary models into one accessible platform

Deployment and Business Model

• Free tier: Basic platform features are available online at no cost, allowing teams to explore core functionality without commitment
• SaaS access: Premium features — including region modification and immunogenicity optimization — are available via a paid SaaS subscription
• Enterprise tier: Deeper integrations are available for enterprise customers, including bespoke data training, enhanced governance features, and commercial exclusivity on specific targets

Deepflare is purpose-built for innovative biotech and pharmaceutical organizations seeking bench-ready, computationally rigorous tools for next-generation vaccine and immunotherapy development. Its combination of prospective validation, intuitive design workflows, and flexible deployment options makes it a compelling platform for teams looking to move beyond legacy binding affinity models.