The traditional paradigm of antibody discovery—reliant on immunization, phage display, or hybridoma screening—has long been constrained by time, cost, and limited diversity. As therapeutic targets become increasingly complex, including membrane proteins, conformational epitopes, and previously "undruggable" antigens, these conventional approaches struggle to keep pace.
At CD ComputaBio, we introduce a next-generation solution powered by the AbGenesis™ Platform, enabling de novo antibody design from first principles. By integrating generative AI, structural biology, and multi-parameter optimization, we design high-affinity, highly developable antibody candidates—without reliance on existing templates.
Traditional antibody discovery has served the field for decades — but it was never designed for the complexity of modern therapeutic targets.
| Pain Point | What It Means in Practice |
| Heavy reliance on immunization or library screening | Limits design freedom; candidates constrained by biological diversity |
| Restricted epitope coverage | Difficult to precisely target conformational or cryptic epitopes |
| Long timelines | Months to years from target identification to validated candidate |
| High attrition rates | Poor developability properties identified too late in the pipeline |
| Challenging antigen classes | GPCRs, membrane proteins, and intrinsically disordered targets remain extremely difficult |
The AbGenesis™ Platform transforms antibody discovery into a computationally driven engineering process, where candidates are designed—not discovered. From structural analysis to validated leads in 3 weeks. At CD ComputaBio, we design custom antibody candidates targeting predefined epitopes with industry-leading speed. Our integrated workflow ensures every sequence meets your exact project specifications:
Our solution combines multiple AI-driven modules:
1. De Novo Sequence Generation
We leverage advanced generative models trained on large-scale antibody datasets to create entirely novel antibody sequences with diverse structural and functional properties.
2. Cross-Reactivity
Anticipate pre-clinical assays with best candidates
3. Hit to Lead
No bad surprise for development with manufacturability specifications
4. Structure-Guided Design
By integrating structure prediction and docking algorithms, we ensure that generated antibodies exhibit optimal antigen-binding conformations.
5. Epitope-Aware Modeling
Target epitopes are explicitly incorporated into the design process, enabling precise targeting and improved specificity.
6. Multi-Parameter Optimization Engine
Simultaneous optimization of:
| Antibody Format | Description | Key Application |
| Full-length IgG | Human / humanized IgG1, IgG2, IgG4 | Therapeutic antibody development |
| scFv | Single-chain variable fragment | Intrabodies, CAR-T constructs, diagnostics |
| Fab | Fragment antigen-binding | Structural studies, therapeutic fragments |
| Nanobodies (VHH) | Single-domain camelid-derived antibodies | Challenging targets, high tissue penetration |
| Bispecific Antibodies | Dual-target binding, multiple formats | T-cell engagers, co-receptor targeting |
| Epitope-specific Antibodies | Precision targeting of defined epitopes | Epitope-specific mechanistic studies |
| High-Affinity Variants | AI-driven affinity maturation of existing leads | Lead optimization, potency enhancement |
To ensure real-world performance, we provide optional experimental validation services:
| Target | Format | Lab Results (independent) | Design Rounds | Panel Size | Success Rate |
| Chemokine receptor — confidential membrane target involved in immune cell signaling | scFv | 23 binders in the 50–100 nM range obtained across 3 iterative design rounds; finalists identified from 96 designs | 3 | 96 | 24% |
| Dipeptidyl peptidase IV — peptidase enzyme | VHH-Fc | 2 functional binders confirmed from a single round of 12 designs | 1 | 12 | 18% |
| Immunomodulatory receptor — confidential target implicated in lymphocyte activation | IgG | 25 specific clones discovered (100 designs screened; comparable yeast-display library screen yielded similar diversity) | 2 | 100 | 26% |
| Neuronal protein — alpha-synuclein conformation-specific binder for synaptic vesicle trafficking studies | IgG | 2 conformation-specific binders produced (1 binder validated out of 5 candidates advanced; designs drawn from a 96-sequence pool) | 1 | 5 | 30% |
| Deliverable Category | Specific Outputs | Description |
| Antibody Sequences | Top-ranked de novo antibody sequences | AI-generated, fully novel antibody candidates optimized for target binding |
| Sequence Diversity Set | Candidate panels (10–100+ sequences) | Diverse sequence pool covering multiple binding modes and epitope interactions |
| Structural Models | Antibody–antigen complex structures | High-confidence 3D models of binding interactions |
| Binding Affinity Predictions | KD / binding score predictions | Quantitative evaluation of antibody-target interactions |
| Epitope Mapping Insights | Predicted binding regions | Identification of antibody binding sites on antigen |
| Developability Assessment Report | Stability, aggregation, solubility, immunogenicity | Multi-parameter evaluation of drug-like properties |
| Design Rationale Documentation | AI-driven design explanation | Transparent explanation of why candidates were selected |
| Experimental Validation Data (Optional) | Expression, SPR/BLI, functional assay results | Wet-lab validation of selected candidates |
| Project Summary Report | Final comprehensive report | End-to-end documentation of workflow, results, and recommendations |
| Follow-Up Optimization Plan | Suggested next-step strategy | Recommendations for further affinity maturation or engineering |
We begin with a detailed scientific consultation to define target class, epitope preferences, antibody format, and key developability requirements. A customized project plan and timeline are provided.
Antigen analysis and epitope selection are confirmed in collaboration with the client team, with clear documentation of design constraints.
AbGenesis™ runs the full generative and optimization pipeline. Interim results are shared at defined milestones for client review and feedback.
Based on milestone feedback and early experimental results, design parameters are refined and additional optimization cycles are executed as needed.
Final candidates are validated experimentally (where applicable), and all deliverables are packaged in a structured final report with full data documentation.
Choosing a design partner is not only about algorithms—it is about execution, scientific judgment, and translational relevance.
With CD ComputaBio, clients gain access to a partner focused on making AI-assisted antibody design usable, measurable, and project-ready.
A client required rapid antibody generation for a novel oncology target.
AI-driven sequence generation and structure-based optimization were applied.
Lead candidates generated within 3 weeks
Binding affinity improved by >5× through iterative optimization
Immediate progression to functional assays
Existing antibody candidates showed aggregation and stability issues.
AbGenesis™ applied multi-parameter optimization to redesign sequences.
Aggregation reduced by 20%
Thermal stability increased significantly
Improved manufacturability
The AbGenesis™ Platform redefines antibody discovery by integrating generative AI, structural biology, and experimental validation into a unified workflow. By enabling de novo antibody design, we empower researchers and biotech companies to move faster, reduce risk, and unlock previously inaccessible targets. Contact us today to accelerate your target discovery program.
