AI: The Most Disruptive Force in Modern Immunology

AI is arguably one of the most disruptive innovations in human history, revolutionising every aspect of our lives — and immunology is no exception.

We have reached a point where in silico design of T cell receptors that recognise and eliminate tumours, or the identification of cancer neoantigens for personalised vaccines, are no longer science fiction but scientific reality.

Experimental technologies once considered indispensable — such as X-ray crystallography or cryo-EM — are now being complemented or even replaced by AI-driven protein structure prediction models.

But with transformative power comes a challenge: the inability to adapt, to harness AI’s full potential, risks creating long-lasting disparities in research capability and control.

At ImmSilico, we help you turn this challenge into opportunity. We guide you in selecting and applying the right AI models for your specific questions, identifying the expertise you need, and integrating these capabilities into your research. Our mission is to ensure you not only keep pace with this revolution — but lead it.

AI models like AlphaFold 3 are revolutionizing immunology — revealing protein structures, decoding immune recognition, and accelerating precision therapies at an unprecedented pace.

Our AI-Powered Services

Advanced AI and integrative solutions of single cell and spatial data

  • How to align high-dimensional sparse data generated by different technologies, eg, scRNA, Visium  (sequence-based) and/or Xenium(image-based)

  • Implement systematic data imputation to recover missing signals.

  • Advanced solutions to translate transcriptomics/epigenomics/spatial data into diagnosis, prognosis, treatment choice, pathogenesis and therapeutic discovery

  • Deep learning/graph-based methods for immune dynamics

AI-driven strategies for immune repertoire dynamics and disease association

  • Systematic investigation of immune repertoire architecture, dynamics and disease association.

  • Identifying antigen-specific or disease-specific TCRs/BCRs and their spatio-temporal dynamics, eg, blood to tissue trafficking or over the course of vaccination/disease

  • Cutting-edge strategies to deorphanise orphan TCRs

  • In silico inference of TCR:pMHC structures, investigating impact of mutations on structural dynamics of these immune complexes

  • In silico design of TCR, CAR-T cells and/or target antigens

  • In silico identification of T cell targets in cancer patients

  • In silico identification of self-antigen targets in autoimmune disease

Public data repositories, and open access analytical resources

  • Integration of and/or alignment of your data with atlas-level public data

  • Benchmarking and model performance strategies using known resources (e.g., ImmGen, Human Cell Atlas)

Advanced Visualisation & Interpretation

  • Advice on biologically meaningful dimension reduction strategies, latent space coding and decoding etc

  • Interpret spatial and subclonal tissue architecture

  • Support scientific storytelling around AI findings

Use Case and Track Record

• Developed predictive TCR clustering to infer antigen specificity in tumour-reactive CD8+ T cells

• Designed multi-modal AI analysis of CPI-bound vs CPI-free T cell niches using CosMx & CITE-seq

• Implemented spatial niche identification to map regeneration zones in colonic crypts from IBD tissue

Turn Complex Data into Actionable Immunology Insights

We help you design the right AI and analytical strategies to navigate the complexity of immunological data—its high dimensionality, sparsity, noise, and entangled technical and biological variation.

Our consulting ensures you ask the right questions, choose the right tools, and build the right team—so your data reveals insight, not confusion.

Contact Us: contact@immsilico.com