Lighting the Path to Cures with AbGenesis™ Platform

End-to-end AI antibody discovery from hit generation to lead optimization
Tackling previously undruggable targets with novel mechanisms of action
Seamlessly integrating proprietary computational models with wet lab capabilities

Partner With Us Explore Platform

# Antibody Discovery # Generative AI # Multispecifics # Hard Targets # De Novo Design # Structural Prediction # Wet Lab Integration # Oncology # Immunology # Biotherapeutics # Antibody Discovery # Generative AI # Multispecifics # Hard Targets # De Novo Design # Structural Prediction # Wet Lab Integration # Oncology # Immunology # Biotherapeutics # Antibody Discovery # Generative AI # Multispecifics # Hard Targets # De Novo Design # Structural Prediction # Wet Lab Integration # Oncology # Immunology # Biotherapeutics

About Us

CD ComputaBio stands at the forefront of biologics innovation, leveraging AI-driven solutions to engineer the next generation of biotherapeutics. By synergizing proprietary computational algorithms with high-throughput wet lab validation, we unlock the potential to address "undruggable" targets and pioneer molecules with superior therapeutic efficacy.

AbGenesis™ Platform

End-to-End AI-Driven Platform: Our platform seamlessly bridges the gap between lead compound discovery and optimization. It maximizes therapeutic differentiation for each molecule by leveraging advanced computational intelligence.
Industry-Leading AI Models: At the core of our platform are models trained on our proprietary biologics database to address complex biological mechanisms and accelerate the development of next-generation biologics.

Capabilities

Core Application Scenarios

Deploying AbGenesis™ to unlock superior therapeutic differentiation across critical disease areas.

Hard Targets Discovery

Uncover superior hit antibodies targeting challenging biological mechanisms, including GPCRs and ion channels.

Epitope-focused discovery
Rare clone identification
Conformational specificity

Advanced Engineering

Expand molecular engineering possibilities to achieve true multi-parametric optimization (optimizing >7 parameters simultaneously).

Affinity maturation
Developability enhancement
Viscosity & solubility tuning

Multispecifics Design

Enable complex molecular architectures to unlock synergistic biology and address multi-pathway diseases like immune disease and cancer.

Common light chain generation
scFv stability optimization
Multi-valent formatting

pH-Sensitive Engineering

Rationally design antibodies with conditional binding to target the tumor microenvironment or enable antigen recycling.

Intramolecular pH bridge design
Endosomal release kinetics
Half-life extension (YTE)

De Novo Antibody Design

CD ComputaBio leverages advanced computational modeling and artificial intelligence algorithms to provide end-to-end de novo antibody design services.

Target-Specific CDR Design
Affinity Maturation
Sequence Humanization

Immunogenicity Resolution

Overcome clinical hurdles like high ADA (Anti-Drug Antibody) responses by rationally designing binding stoichiometry and complex formation.

Trimer destabilization
Immune complex prevention
Stoichiometry control

Technology

The Three Engines of AbGenesis™

Our platform is fueled by a proprietary, multi-modal database incorporating both positive data and negative developmental outcomes for superior model training.

Structural Prediction Engine

Our proprietary protein complex structure prediction algorithm accurately predicts the 3D complex structure starting solely from the primary sequence of an antibody and an antigen. This serves as the indispensable foundation for all downstream rational engineering and optimization.

High-resolution antibody-antigen docking.
Accurate resolution of primary intact states.
Molecular dynamics simulations for conformational shifts.

Generative Design Suite

Operating similarly to advanced Large Language Models (LLMs), our generative tools accept "prompts" and "constraints" (such as structural boundaries or developability targets). The AI then produces novel antibody sequences that not only meet the required constraints but are natively highly developable.

Zero-shot sequence generation.
Multi-constraint prompt engineering.
Trained on vast sets of highly functional biologic sequences.

Predictive Selection Suite

Once large sequence pools are generated, our predictive models act as a high-throughput computational filter. We rapidly down-select sequences, accurately identifying the rare candidates that simultaneously fit the strict multi-parametric requirements necessary for modern clinical programs.

In silico developability assessment.
Viscosity and aggregation prediction.
Rapid filtering of NGS repertoires to find rare functional clones.

Why Partner with Us?

Delivering best-in-class potential through differentiated mechanisms and unified technology.

Differentiated Mechanisms

We don't just find binders; we find function. Our AI can design antibodies that actively destabilize target trimers, preventing large immune complex formations and solving critical issues like clinical ADA responses.

The "Dark Data" Advantage

Our foundation models are trained on exclusive datasets that intentionally include negative outcomes (e.g., molecules with poor developability). This allows our AI to learn what *not* to do, vastly improving accuracy.

Unified Dry & Wet Labs

AI should not exist in a vacuum. Our computational scientists and wet-lab biologists work side-by-side. Predictions are rapidly synthesized and validated in vitro/in vivo, creating a powerful, iterative data loop.

Workflow

Discovery Process

A closed-loop system integrating predictive power with biological reality.

Target Strategy

Evaluating biological rationale and designing AI constraints tailored to overcome specific target hurdles.

Generative Design

In silico creation of massive sequence libraries focused on desired epitopes and required developability profiles.

Empirical Validation

Wet lab synthesis and rigorous in vitro / in vivo screening to validate affinity, functionality, and stability.

Lead Optimization

Applying multi-parametric AI models to further engineer leads for optimal clinical safety and efficacy.

Partnering Models

Flexible engagement strategies to advance transformative medicines together.

Platform Capabilities

Hit Discovery Campaigns: Finding novel binders for targets that have failed traditional display campaigns.
Antibody Optimization: Rescuing promising leads by engineering out liabilities (viscosity, immunogenicity, short half-life).
Bispecific Construction: Leveraging our common light chain technology to create highly developable bispecifics.
Antigen Design: De novo creation of stabilized antigens to direct immune responses in animal campaigns.

Collaboration Types

Strategic Alliances: Long-term partnerships for multi-target drug discovery.
Licensing Agreements: Out-licensing of internally developed, clinic-ready biological assets.
Co-development: Joint risk-sharing models for promising therapeutic hypotheses.
Fee-for-Service Optimization: Targeted campaigns to resolve specific molecular liabilities.

Frequently Asked Questions

Insights into our AI-driven approach to complex biologics design.

How does your AI solve high clinical ADA (Anti-Drug Antibody) responses?

For targets prone to ADA (like certain trimeric cytokines), the ADA response is often driven by the formation of large immune complexes. Our Structural Prediction Engine allows us to perform epitope-focused design. We design antibodies that bind specific regions (like the "crown") that either limit binding stoichiometry or physically destabilize the target trimer into monomers. Monomers cannot form large immune complexes, dramatically reducing the ADA response in vivo.

What is your approach to Bispecific Antibodies?

We employ a multifaceted approach. While we can optimize scFvs for stability and affinity, our flagship solution is our Common Light Chain platform. We utilize our generative AI and predictive models to engineer a single light chain that pairs perfectly with two distinct heavy chains targeting different antigens. This elegantly solves the heavy-light chain mispairing problem and yields bispecifics with native IgG-like developability.

Does the AbGenesis™ platform only operate "in silico"?

Absolutely not. We believe AI in biology is only as good as its empirical validation. We operate a massive, purpose-built wet lab. Once our generative models produce sequence pools, we run them through our predictive suites, and then immediately synthesize and test the top candidates in our own labs. This generates rapid, high-quality data to feed back into our AI models.

Can you engineer antibodies for specific microenvironments?

Yes. We employ pH-sensitive engineering using a structure-guided approach. Our AI doesn't just look for intermolecular pH modulation at the binding interface; it also maps intramolecular pH bridges. By engineering specific residues, we can induce conformational shifts at low pH (like in the tumor microenvironment or endosome), causing the antibody to release the antigen for recycling or conditional activation.