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CellulaAI™

Cell Programming AI Engine

Key Components:

CellulaAI™ represents a cutting-edge AI system that leverages the power of artificial intelligence to transform CAR-T therapy. By optimizing every aspect of the CAR-T design and production process, this engine aims to bring safer, more effective, and personalized cancer treatments to patients worldwide. We can design binder targeting multiple cell specific antigen groups simultaneously. We can design binding molecules that simultaneously target multiple cell-specific antigen groups, including patient-specific neoantigens derived from tumor sequencing results, to achieve personalized cell therapy.

  • Target Antigen Identification
  • ScFv Design and Optimization
  • Constimulatory Domains Selection
  • Off-target and Immunogenicity Screen
  • In Silico Testing and Validation
  • Manufacturing and Scalability Insights
  • Enhanced Efficacy
  • Improved Safety
  • Personalization
  • Scalability

Project Lifecycle

Step 1:
NDA & Project Assessment

Step 2:
Statement of Work & Quote

Step 3:
Sign Contract & Invoice

Step 4:
Upfront Payment &
Target Information for Project Kick-Off

Step 5:
Antibody Designed, Expressed, and Tested

Step 6:
Project Delivered & Milestone Payment

Platform Demos

CellulaAI™
Frequently Asked Questions (FAQs)

What design modules does CellulaAI™ include, and what do they do?
  • Target‑antigen identification – Machine‑learning screens cancer surface‑marker datasets to find tumor‑specific antigens.
  • ScFv design & optimization – Deep‑learning models generate high‑affinity, stable ScFvs trained on antibody–antigen data.
  • Costimulatory‑domain selection – Ranks CD28, 4‑1BB, etc., to maximize activation & persistence.
  • Safety‑feature integration – Embeds suicide genes / off‑switches and simulates cytokine‑release risk.
  • In‑silico testing & validation – Binding affinity and immunogenicity screens with Ainnocence’s engine before wet‑lab work.
  • Manufacturing & scalability insights – Predicts optimal culture / transfection conditions for large‑scale production.
  • Computational design turnaround: a few hours to approximately 2 weeks.
  • Subsequent wet‑lab work (prototype synthesis, testing, scale‑up) follows your workflow and is outside the scope of the deck.

Note: stages often overlap to shorten total timelines.

  • Essential: cancer indication / desired antigen profile.
  • Helpful: patient‑specific genomics, proteomics or clinical‑trial data to personalize designs.
  • Administrative: NDA signature and a short project scope (Project Lifecycle begins with NDA & Project Assessment).
  • Digital package – CAR construct sequences plus simulation read‑outs (binding affinity, safety scores, manufacturability predictions).
  • Prototyping option – synthesis and in‑vitro / in‑vivo testing of top constructs.
  • Manufacturing report – recommended culture and scale‑up parameters for GMP transition.

The engine embeds suicide genes or off‑switch receptors and evaluates their effectiveness through in‑silico simulations, lowering off‑target and cytokine‑release risks.

  • Wet‑lab validated, ultra‑high‑throughput and sequence‑driven platform.
  • Integrates multi‑omics data for personalized CAR design.
  • Provides real‑time manufacturing analytics to enhance batch consistency and regulatory compliance.

No. Ainnocence’s discovery platform is a proprietary, sequence-based AI system developed entirely in-house. By working directly from primary sequence data, it eliminates the need for external 3-D structure-prediction software and other open-source packages. Instead, our technology performs ultra-high-throughput virtual screening and multi-objective optimisation, evaluating up to 10¹⁰ candidates within hours to days—giving our partners capabilities that off-the-shelf tools cannot match.

Interested in how we can support your goals? We’re here to answer any inquiries.

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