Antibody Engineering

Accelerating antibody engineering with in silico solutions
Structure of a populat TNF-alpha blocker showing light chain, heavy chain, and lipophilicity
Structure of a popular TNF-alpha blocker
Context

Generative AI powered in silico antibody engineering

Computational methods allow us to search and design antibodies effectively in a highly expanded space by combining Generative AI, Denoising Diffusion Probabilistic Models (DDPM) and Machine Learning models of downstream properties.Our proprietary algorithms and computational pipeline seamlessly integrate sequence and structure based design considerations, and incorporate multiple property criteria enabling the design of antibodies with improved antigen binding and developability.
Our Solution

Powered by Generative AI

We leverage cutting edge computational technologies including Generative AI, Denoising Diffusion Probabilistic Models and Machine Learning for therapeutic antibody engineering. Our algorithms combine antigen binding affinity, specificity, and stability to produce desired candidate antibodies. Seamlessly integrating sequence, structure, and multiple property criteria, our proprietary pipeline enhances antibody design for improved antigen binding and developability.

Modular, Customizable Pipeline and AI models

Our modular technology can adapt to any antibody discovery stage- starting from disease, antigen, hit, or lead with liabilities. Our experts design antibodies with desired properties from concept to optimization

End-to-end support

We offer comprehensive project support, including customized pipeline development, in vitro antibody validation, and publication assistance

Align antibody design for clinical success

Mitigate candidate risk by assessing manufacturability, immunogenicity, potency, and humanness using our in silico developability toolbox

Accelerated antibody therapeutic development

Our in silico pipeline rapidly generates leads with a higher chance of success in the downstream stages of in vitro, in vivo and clinical testing

In the de novo antibody engineering pipeline, given a synthetic antigen-antibody complex or an existing antigen-antibody complex, the steps are selecting target proteins using ML generative models, concurrent design of antibodies with pluggable conditionalities, evaluation of produced antibodies which includes in silico evaluation of properties and on-demand wet lab evaluation, and finally a report containing a ranked order of antibody sequences. In an existing antibody optimization pipeline, given an existing antibody, the steps are improvement on developmental properties by using conditional generative models, evaluation of optimized properties using in silico tools such as docking, and on-demand in-vitro evaluation of properties.
Antibody Engineering Pipeline
Highlights

Key components & strengths

Sequence and structure generative models

Our generative AI models jointly model sequence and structure, and developability properties, to generate antibodies with strong binding and developability characteristics.

Innovative deep learning and equivariant neural network model

Our models leverage denoising diffusion probabilistic models (DDPM) to broaden the search space of candidate antibodies and address the inherent challenge of antibody property data insufficiency

Multiple antibody formats

Our models span monoclonal, single domain, bispecific, and multispecific antibodies

Customized pipelines

We are able to customize and fine tune our approach and optimize our in silico toolkit to meet the specific needs of each project, whether it is hit identification, hit to lead optimization or developability property optimization

Virtual screening of large antibody libraries

Our technology supports virtual screening of antibodies, analysis of antigen-antibody complex structures, and progressing to final optimized antibody candidates, taking into consideration multiple aspects related to their developability

Our Antibody Engineering offerings

Antibody Virtual screening

In silico pipeline to evaluate a large library of antibody candidates and produce rank order list of hits based on binding affinity and developability properties.
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Antibody Lead Optimization

Generative AI based model to optimize properties such as humanization, aggregation optimization, stability improvement, etc. for increased precision.
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Antibody Characterization

In silico developability property profile of the antibody including thermal stability, isoelectric point identification, hydrophobic patches, aggregation propensity, humanization score, etc.
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Sequence Structure Co-Modeling

Explore the unexplored designable antibody space with generative AI models while considering sequence and structural co-modeling to design antibodies for a target antigen of interest.
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Sample Use Cases

Outcomes

Accelerate Antibody Therapeutics: R&D Enhanced by in silico Solutions

Revolutionize antibody design through structure guidance

Unlock previously unexplored antibody sequences using our state of the art generative AI and structure-based discovery process

Maximize antibody affinity

Predict higher affinity antibodies through affinity maturation, unlocking their full therapeutic potential

Achieve precision target recognition

Accurately predict antibody developability, humanness, and affinity to target antigens

Closed loop with in vitro testing

Combine in silico and experimental testing (in vitro, in vivo) to accelerate antibody research while reducing experimental expenditure

Discover our offerings across the biopharma value chain

Our Solutions

Customizable platforms and solutions across the drug discovery value chain from target discovery to therapeutic development
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Our Services

Offering services in computational sciences and technology to complement biopharma R&D
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Case Studies

Helping clients accelerate drug discovery and development by building computational biology, computational chemistry, technology, and cloud solutions
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Learn more about our Antibody Engineering

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