Platform

Platform

The immune system is at the center of human health and disease

Overview

Our AI-driven, end-to-end platform leverages single-cell multiomics, integrating vast preclinical and clinical data to deliver actionable insights for drug discovery and development.

Integrated,
end-to-end platform

Our integrated end-to-end platform components are designed to provide actionable clinical decision support, from therapeutic query to actionable recommendation.

  • Generate

    We generate high-quality, multi-omic data from clinical and preclinical experimental samples using cutting-edge wet lab and computational pipelines.

    • What: Using clinical blood and tumor/tissue samples, in vitro and ex vivo generated samples we apply single-cell multio-mics sequencing (whole transcriptome (RNA), surface proteome, TCR/BCR repertoire, and spacial transcriptomics)
    • Why: Multi-omic measurements generate a high resolution data foundation for downstream ML-based analysis

  • Augment

    Generated data is enriched with AMICA, the world’s largest immune-focused, harmonized single-cell database.

    • What: AMICA is is a continuously growing proprietary dataset library composed of harmonized data derived from internal experiments, ongoing academic partnerships and public data
    • Why: Provides direct comparison of immune features to standard of care clinical and preclinical datasets

  • Compute

    Leveraging our advanced machine learning platform, we compute novel immune features, linking immune mechanisms to treatment responses and outcomes.

    • What: Our Immunodynamics engine is an analytical framework that identifies and prioritizes immune activation features in relation to biological questions and desired clinical outcomes
    • Why: The IDE framework extracts meaningful biological signals related to a specific biological question, providing mechanistic insights that describe the observed immune process

  • Validate

    Leveraging functional genomics, we validate ML-driven hypotheses in the lab to ensure robust and actionable insights

    • What: In vitro evaluation of ML-driven hypotheses in disease-relevant model systems with functional readouts
    • Why: Lab validation ensures that IDE-derived insights and therapeutic hypotheses are biologically actionable, supporting their relevance in preclinical and clinical contexts

  • Recommend and Explain

    Our recommendation engine provides clear, actionable paths forward – how to proceed and why, in order to increase confidence in decision making.

    • What: We distill immune complexity and visualize insights related to each biological question in a scorecard format
    • Why: Our IDE is no black box – we provide the data underlying our recommendations

AMICA – the world’s largest cell-level immune knowledge base

  • Deep, multiomic

    Single cell profiling of clinical cohorts

  • Dynamic

    Functional genomics platform for multiplexed perturbations

  • Broad, curated

    Data from public sources

  • Unified

    Clinical and immunological annotations for integrative analysis

  • 5-6k

    Studies

    • 350+ Sc studies

  • 800+

    Cell types

    • 80 Immune cell types

  • 500+

    Diseases

  • 300K+

    Patient samples

Immunai data foundation

AMICA, our Annotated Multiomic Immune Cell Atlas, is the data foundation that powers our drug discovery and development efforts.
 AMICA is comprised of high resolution single-cell data from public and proprietary clinical cohorts and experiments across tens of millions of cells. The use of harmonized metadata and ontologies, stringent quality control, accurate cell-type annotation, data normalization, and peer-review by domain experts, creates a data foundation that enables high quality computations and machine learning analytics.

The ImmunoDynamics Engine (IDE) is our AI model designed to enhance clinical decision-making

Prioritize trial arm or patient subgroup

Validate therapeutic hypotheses with deep mechanistic insight

Identify candidate patient stratification biomarkers

Add confidence to preclinical efficacy and safety evaluation

Identify targets with the highest potential for therapeutic impact