Applications
At WhiteLab Genomics, we empower our partners to optimize genomic medicine development through in silico methods that reduce costs and accelerate timelines. Our AI-driven approach accelerates the development of breakthrough solutions in genomic medicine, saving resources while delivering precise, effective therapies.
Target Receptor Identification
Enhanced Editing: Identify genomic sites for precise editing with minimal off-target effects.
Capsid Engineering: Develop immune-evading, cell-specific, and efficient gene delivery vectors.
Powered by Protein Language Models & Deep Learning
Vector Engineering
Peptide Engineering: Generate high-affinity vectors for targeted delivery.
In Silico Optimization: Utilize data-driven methods for advanced structural biology analysis.
Enabled by Generative AI, Machine Learning & Physics based Algorithms
Payload Design
- Functional Modulation: Optimize activity and tissue specificity of therapeutic molecules.
- Reduced Side Effects: Ensure efficacy while minimizing adverse effects.
Powered by Generative AI
Cell Therapy
- Antigen Targeting: Select precise antigens and binding sites.
- Improved Activity: Create CARs with enhanced on-target performance and lower cytotoxicity. Leveraging Reinforcement Learning
Bioproduction
- High-Yield Vectors: Identify and enhance traits for optimal production.
- Process Optimization: Refine gene expression, metabolism, and growth rates.
Guided by Biostatistics
