Our Solution
At immunitoAI, we believe in a "Design over Discovery, Drug First, Structure First" philosophy. Our AI-platform is created to generate not just antibodies, but target specific antibody-based drug molecules.
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.
Visit our blog to learn more about the technology.
imDESIGN - Design Novel Antibodies
We have successfully developed a platform called imDESIGN that generates novel antibodies and antibody fragments. We break down problem statements to the atomic level for effective data generalisation and mathematical transformation. Our neural networks learn the structural complementarity of epitopes (binding region of the antigen) with paratopes (binding region of the antibody). 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.
Please contact us at imDESIGN@immunitoai.com for industry collaborations.
imRANK® - Screen and Rank Antibodies
Our imRANK® system complements imDESIGN by screening and ranking the antibodies generated by imDESIGN, based on drug properties and specificity. Both structural and sequential drug developability properties are assessed at this stage, and the final molecules that fulfil all the required criteria are selected for experimental validation.
Please contact us at imRANK@immunitoai.com for industry collaborations.
In-House Biological Validation
Biological validation of designed antibodies is a crucial step in our process. We transfer AI-generated antibody DNAs to our in-house biological lab for antibody production, purification, and characterization for target binding and specificity. A feedback loop integrates experimental data into our proprietary dataset for neural network re-training and dataset enhancement. This continuous improvement refines our biological and mathematical hypotheses, enhancing network performance through improved data accuracy.
Our Approach
Our AI-driven approach stands on three foundational pillars: Drug-First Antibody, Structure-First AI Platform
Design over Discovery
immunitoAI generates novel antibodies entirely from scratch, without relying on biological sources like animals or humans. We have built an AI platform for de novo intentional design of optimised antibodies, rather than screening from a pool of antibodies and converting them into drugs. Computational design enables rapid exploration of vast antibody sequence space, overcoming constraints imposed by biological systems, such as limited diversity and randomness. Our approach accelerates timelines while enhancing efficiency and success rates, offering a sustainable solution for antibody discovery.
Drug-First Antibodies
Our AI-generated antibodies are precisely designed as ideal drug candidates from their inception. Instead of starting with biological leads and undergoing lengthy optimization, we embed drug-like characteristics into antibody sequences from the outset. This approach allows us to tailor antibodies with pre-defined drug properties like stability, affinity, and specificity. Additionally, these properties are assessed after the sequence generation stage through multiple layers of sequential and structural drug developability assessments, even before the molecules reach the lab. Consequently, we reduce late-stage failures and enhance the likelihood of clinical success.
Structure-First AI Platform
Our AI platform is developed with a revolutionary Structure-First philosophy for antibody design. We believe that understanding the structural basis of antibody-antigen interactions is paramount for developing potent and selective therapeutics. This proprietary system employs neural networks trained to understand the complex rules governing structural complementarity between antibodies and target antigens. By designing antibodies with precise target binding, we minimise off-target interactions and potential side effects, while computationally enhancing desired binding characteristics like affinity and specificity. This approach expands the druggable space, particularly for rare diseases and undruggable targets.