Our Solution
Our Solution
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At immunitoAI, we believe in a "Biology First, Drug First, Intelligence Driven" philosophy. Our AI-platform, AbInitio™, is created to generate not just antibodies, but target specific first-in-class/best-in-class antibody-based drug modalities.
At the core of our methodology lies a powerful AI pipeline comprising multiple neural networks designed to predict biologically viable antibodies. Through rigorous biological experimentations, we ensure that all our predictions are lab validated.
Our algorithms learn the complementary structures of epitopes and paratopes, enabling the reconstruction of target specific antibodies. By incorporating drug developability characteristics from the very start, we are paving the way for the development of next-generation antibody therapeutics.
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AbInitio™ - High Efficiency AI-platform Designing Superior Antibody Drugs.
immunitoAI’s proprietary generative AI platform designs novel antibodies and antibody fragments. Our deep neural networks learn the structural complementarity of paratopes* with their epitopes*. By prioritising the three dimensional structural complementarity, our AI platform can precisely sculpt antibodies for each epitope to maximise favourable and specific interactions with the target antigen. Through key mathematical transformations of the two interacting proteins, our deep neural networks can generalise structural and sequential complementarity, crucial for critical function. Our AI-platform assesses drug developability properties at the design stage, and the final molecules that fulfil all the required criteria are selected for experimental validation.
* Paratope: Binding region of the antibody* Epitope: Binding region of the antigen
Biological validation of designed antibodies is a crucial step in our process. We transfer AI-generated antibody sequences to our in-house biological lab for detailed characterization on target binding, specificity and drug properties. A feedback loop integrates experimental data into our proprietary dataset for neural network re-training and dataset enhancement. This continuous improvement refines biological and mathematical hypotheses, enhancing network performance through improved data accuracy.
Our Approach
Biology defines the target, drug properties define the design — AbInitio™, our in-house AI platform, turns both into epitope-specific antibodies
Biology First
Every antibody we design starts with the biology, not the sequence space. Our Biologics Team defines the epitope and mechanism of action before a single molecule is generated — target, epitope, and what the antibody must do once it binds are set by a deep understanding of the disease itself. AbInitio™ then designs directly against that epitope, enabling truly epitope-specific antibody generation across any protein target, including those historically considered undruggable. This target-down approach — generating de novo rather than screening a pool of existing antibodies — lets us explore vast antibody sequence space and pursue rare diseases and novel biology that biological discovery, constrained by naturally occurring dive
Drug First
We design to a drug profile, not just a binder — first-in-class where no precedent exists, best-in-class where one does. Rather than starting from a biological lead and optimizing it over months, we embed drug-like characteristics — stability, affinity, specificity — into the antibody sequence from the outset. Antibody format and drug modality follow from the biology: monovalent, multivalent, or multi-specific, engineered for the potency and safety window the target demands. These properties are assessed immediately after sequence generation, through multiple layers of sequential and structural developability screening, before a molecule ever reaches the lab — reducing late-stage failures and improving the odds of clinical success.
Intelligence Driven
AbInitio™ comprises foundational AI models built entirely in-house, enabling the generation of biologically viable antibody sequences within weeks. The platform is built on a Structure-First philosophy: understanding the structural basis of antibody-antigen interactions is what makes potent, selective therapeutics possible. Neural networks trained to model structural complementarity between antibody and target enable epitope-specific, novel design across all protein targets, for both agonism and antagonism. By designing for precise target binding computationally, we minimize off-target interactions and side effects while expanding the druggable space — particularly for rare diseases and undruggable targets.
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