Apotek Genesis "Antibody Redesign": Expediting Antibody Discovery

Design-Predict-Rank: A decision-oriented framework for CDR redesign, which incorporates structure-aware evaluation and prioritizes antigen affinity.

Antibody CDR Redesign, Evaluation and Prioritization

Key Takeaways:

• The monoclonal antibody market continues to dominate the biopharmaceutical landscape, yet optimizing lead antibodies for improved binding affinity remains a time-consuming and resource-intensive process.

• Apotek Genesis "Antibody Redesign" is a platform-based antibody optimization solution that enables rapid exploration of CDR sequence space, structural prediction, and binding affinity ranking—accelerating the path from lead antibody to optimized therapeutic candidate.

• With this protocol, users can potentially: (i) increase antigen affinity with their lead antibody, and (ii) expand the therapeutic utility of existing antibody assets.

What's So Unique About Apotek's Genesis Solution

Current Challenge

Antibody affinity maturation traditionally relies on labor-intensive experimental approaches such as phage display or yeast surface display, requiring multiple rounds of selection and screening. These methods are:

• Time-consuming (weeks to months per optimization cycle)

• Limited in sequence space exploration

• Expensive to execute at scale

• Dependent on library quality and selection pressure

The industry needs a computational-first approach that can rapidly generate, evaluate, and prioritize antibody variants before committing to wet lab resources.

Our Capabilities

Apotek Genesis "Antibody Redesign" delivers an end-to-end computational antibody optimization workflow.

• Validated Input Processing: When approached with an input set (antibody sequence with CDR annotations), we focus on validating the sequence format and ensuring the target antigen exists in our library. This provides clean and ready-to-process antibody data for downstream analysis. Our library currently supports 30 most-common therapeutic antigens—thoroughly benchmarked against all in-trial monoclonal antibodies.

  
  

• State-of-the-Art CDR Generation: CDR mutation is the key to antibody affinity maturation. We utilize a state-of-the-art generative model to create diverse CDR variants while maintaining antibody framework integrity. This allows exploration of sequence space for improved binding candidates. Users can:

  • Select single or multiple CDRs for redesign

  • Configure mutation rates and novelty constraints

  • Generate thousands of variants for comprehensive sampling

  
  

• Structure-Informed Analysis: The structural information of variants is essential for accurate antibody-antigen docking and ΔΔG estimation. Our pipeline generates high-confidence structural predictions for each variant.

  
  

• Precision Docking: Docking simulates the antibody-antigen interaction, providing structural insight into binding mode and interface contacts. This step is crucial for understanding how the redesigned antibody physically interacts with its target.

  
  

• Energy-Based Ranking: We evaluate binding improvement using multiple energy-based metrics. Candidates are ranked by their predicted binding affinity changes (ΔΔG), allowing identification of the most promising antibody variants for experimental validation.

Case Study: NeurIPS 2024 Protein Binder Competition

Our approach was validated in the NeurIPS 2024 Protein Binder Design Competition hosted by Adaptyv Bio, where Vizuro was selected for wet lab validation.

This competitive validation demonstrates the real-world applicability of our computational antibody optimization pipeline against industry benchmarks.

Read the full case study →

Summary: Prioritization & Resource Optimization

The ability to computationally screen thousands of antibody variants enables:

• Focused experimental validation—prioritize only top-ranked candidates for wet lab testing

• Reduced development cycles—compress months of iterative optimization into days

• Data-driven decision making—quantitative binding predictions guide resource allocation

• Multi-objective optimization—balance binding affinity against stability and developability

Apotek Can Help

Boost Market Value → Expanded IP Portfolio

Novel CDR sequences with improved binding represent new intellectual property opportunities. Redesigned antibodies can:

• Extend patent protection for existing therapeutic programs

• Create differentiated follow-on biologics

• Enable new indication expansion for established targets

Risk Assessment & Validation Strategy

Our ranked output enables smart experimental design:

• Prioritize candidates with the strongest predicted improvements

• Identify potential liabilities early in the optimization process

• Design focused validation panels rather than broad screening campaigns

• Make go/no-go decisions before committing significant wet lab resources

About Apotek

Apotek harnesses groundbreaking Causal AI and GenAI technologies to build an integrated platform that transforms multi-omics data into actionable insights — enabling novel target discovery, bio-simulation, and generative drug design. By uncovering cause-and-effect relationships rather than spurious correlations, our approach enhances biological interpretability, improves model robustness, and accelerates decision-making across the drug discovery pipeline.

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