Bio AI
<h2>Biology Meets AI for
Smarter Research, Design
and Production
Bio AI is transforming how companies design molecules and genetic sequences, interpret complex data and scale data-driven production. Together we develop high-value AI use cases, select suitable models and turn promising ideas into operational workflows.
Book a first consultation
Explore Use Cases
Where AI Creates Value
Bio AI is most valuable when it is connected to a clear scientific or operational goal. EVOBYTE helps
companies identify use cases where AI can reduce iteration cycles, improve decision-making or optimize processes.
Faster Candidate
Exploration
Generate and prioritize larger design spaces before experimental testing.
Improved R&D Focus
Use AI to rank hypotheses, screen targets and focus laboratory resources on the most promising candidates.
More Reproducible
Workflows
Turn exploratory notebooks and manual analyses into documented, maintainable AI pipelines.
Integration into Existing
Infrastructure
Connect models with databases, cloud environments, dashboards, APIs and internal tools.
Process Parameter Optimization
Use AI to create digital twins for production and manufacturing, generating ROI through AI optimized processes
From first AI Use Cases Towards Working Solutions
Our structured methodology ensures that AI integration is not just a pilot, but a core driver of your scientific success.
01
Consultation
Deep-dive into your biological
data landscape and identify
high-impact AI opportunities.
Audit existing data
Gap analysis
02
Workflow Design
Designing the solution
blueprint. We map algorithms
to your specific use case.
Pipeline architecture
Algorithm selection
03
Prototype
Building and training the
model. Rapid prototyping with
real-world scientific datasets.
Model developement
Real world validation
04
Scale
Full-scale deployment into
your production or research
environment with MLOps.
Lifecycle management
Workflow integration
Scientific Use Cases
Deep-dive into specialized biological domains where our AI-driven approach delivers
measurable research outcomes.
Use Case 01
Protein and Molecular Design
APPLICATIONS
De novo protein design, antibody engineering and ligand binding optimization for therapeutic discovery and biotechnology.
CORE MODELS
Alphafold / OpenFold, ProteinMPNN, RFdiffusion combined with physics engines like Gromacs
<svg aria-hidden="true" viewBox="0 0 512 512" xmlns="http://www.w3.org/2000/svg"><path d="M496 384H64V80c0-8.84-7.16-16-16-16H16C7.16 64 0 71.16 0 80v336c0 17.67 14.33 32 32 32h464c8.84 0 16-7.16 16-16v-32c0-8.84-7.16-16-16-16zM464 96H345.94c-21.38 0-32.09 25.85-16.97 40.97l32.4 32.4L288 242.75l-73.37-73.37c-12.5-12.5-32.76-12.5-45.25 0l-68.69 68.69c-6.25 6.25-6.25 16.38 0 22.63l22.62 22.62c6.25 6.25 16.38 6.25 22.63 0L192 237.25l73.37 73.37c12.5 12.5 32.76 12.5 45.25 0l96-96 32.4 32.4c15.12 15.12 40.97 4.41 40.97-16.97V112c.01-8.84-7.15-16-15.99-16z"></path></svg>
<h4>
VALUE
</h4>
Reduction in experimental screening cycles, exploration of new designs and better priorization
<img width="1024" height="572" src="https://evo-byte.com/wp-content/uploads/2026/05/protein_design-1024x572.webp" alt="" srcset="https://evo-byte.com/wp-content/uploads/2026/05/protein_design-1024x572.webp 1024w, https://evo-byte.com/wp-content/uploads/2026/05/protein_design-300x167.webp 300w, https://evo-byte.com/wp-content/uploads/2026/05/protein_design-768x429.webp 768w, https://evo-byte.com/wp-content/uploads/2026/05/protein_design.webp 1376w" sizes="(max-width: 1024px) 100vw, 1024px" />
Use Case 02
<h3>Sequence and Vector Optimization</h3>
<svg aria-hidden="true" viewBox="0 0 512 512" xmlns="http://www.w3.org/2000/svg"><path d="M160 320h12v16c0 8.84 7.16 16 16 16h40c8.84 0 16-7.16 16-16v-16h12c17.67 0 32-14.33 32-32V64c0-17.67-14.33-32-32-32V16c0-8.84-7.16-16-16-16h-64c-8.84 0-16 7.16-16 16v16c-17.67 0-32 14.33-32 32v224c0 17.67 14.33 32 32 32zm304 128h-1.29C493.24 413.99 512 369.2 512 320c0-105.88-86.12-192-192-192v64c70.58 0 128 57.42 128 128s-57.42 128-128 128H48c-26.51 0-48 21.49-48 48 0 8.84 7.16 16 16 16h480c8.84 0 16-7.16 16-16 0-26.51-21.49-48-48-48zm-360-32h208c4.42 0 8-3.58 8-8v-16c0-4.42-3.58-8-8-8H104c-4.42 0-8 3.58-8 8v16c0 4.42 3.58 8 8 8z"></path></svg>
<h4>
APPLICATIONS
</h4>
Codon optimization, promotor design and vector sequence generation for higher yield and specific expression
<svg aria-hidden="true" viewBox="0 0 576 512" xmlns="http://www.w3.org/2000/svg"><path d="M208 0c-29.9 0-54.7 20.5-61.8 48.2-.8 0-1.4-.2-2.2-.2-35.3 0-64 28.7-64 64 0 4.8.6 9.5 1.7 14C52.5 138 32 166.6 32 200c0 12.6 3.2 24.3 8.3 34.9C16.3 248.7 0 274.3 0 304c0 33.3 20.4 61.9 49.4 73.9-.9 4.6-1.4 9.3-1.4 14.1 0 39.8 32.2 72 72 72 4.1 0 8.1-.5 12-1.2 9.6 28.5 36.2 49.2 68 49.2 39.8 0 72-32.2 72-72V64c0-35.3-28.7-64-64-64zm368 304c0-29.7-16.3-55.3-40.3-69.1 5.2-10.6 8.3-22.3 8.3-34.9 0-33.4-20.5-62-49.7-74 1-4.5 1.7-9.2 1.7-14 0-35.3-28.7-64-64-64-.8 0-1.5.2-2.2.2C422.7 20.5 397.9 0 368 0c-35.3 0-64 28.6-64 64v376c0 39.8 32.2 72 72 72 31.8 0 58.4-20.7 68-49.2 3.9.7 7.9 1.2 12 1.2 39.8 0 72-32.2 72-72 0-4.8-.5-9.5-1.4-14.1 29-12 49.4-40.6 49.4-73.9z"></path></svg>
<h4>
CORE MODELS
</h4>
Deep leaning and transformer-based models like Evo2 or AlphaGenome for sequence and regulatory predictions
<svg aria-hidden="true" viewBox="0 0 512 512" xmlns="http://www.w3.org/2000/svg"><path d="M496 384H64V80c0-8.84-7.16-16-16-16H16C7.16 64 0 71.16 0 80v336c0 17.67 14.33 32 32 32h464c8.84 0 16-7.16 16-16v-32c0-8.84-7.16-16-16-16zM464 96H345.94c-21.38 0-32.09 25.85-16.97 40.97l32.4 32.4L288 242.75l-73.37-73.37c-12.5-12.5-32.76-12.5-45.25 0l-68.69 68.69c-6.25 6.25-6.25 16.38 0 22.63l22.62 22.62c6.25 6.25 16.38 6.25 22.63 0L192 237.25l73.37 73.37c12.5 12.5 32.76 12.5 45.25 0l96-96 32.4 32.4c15.12 15.12 40.97 4.41 40.97-16.97V112c.01-8.84-7.15-16-15.99-16z"></path></svg>
<h4>
VALUE
</h4>
Optimal production yields, performant vectors and specific expression in target systems
<img width="1024" height="572" src="https://evo-byte.com/wp-content/uploads/2026/05/vector_design-2-1024x572.webp" alt="" srcset="https://evo-byte.com/wp-content/uploads/2026/05/vector_design-2-1024x572.webp 1024w, https://evo-byte.com/wp-content/uploads/2026/05/vector_design-2-300x167.webp 300w, https://evo-byte.com/wp-content/uploads/2026/05/vector_design-2-768x429.webp 768w, https://evo-byte.com/wp-content/uploads/2026/05/vector_design-2.webp 1376w" sizes="(max-width: 1024px) 100vw, 1024px" />
Use Case 03
<h3>AI for Single-Cell Research</h3>
<svg aria-hidden="true" viewBox="0 0 512 512" xmlns="http://www.w3.org/2000/svg"><path d="M160 320h12v16c0 8.84 7.16 16 16 16h40c8.84 0 16-7.16 16-16v-16h12c17.67 0 32-14.33 32-32V64c0-17.67-14.33-32-32-32V16c0-8.84-7.16-16-16-16h-64c-8.84 0-16 7.16-16 16v16c-17.67 0-32 14.33-32 32v224c0 17.67 14.33 32 32 32zm304 128h-1.29C493.24 413.99 512 369.2 512 320c0-105.88-86.12-192-192-192v64c70.58 0 128 57.42 128 128s-57.42 128-128 128H48c-26.51 0-48 21.49-48 48 0 8.84 7.16 16 16 16h480c8.84 0 16-7.16 16-16 0-26.51-21.49-48-48-48zm-360-32h208c4.42 0 8-3.58 8-8v-16c0-4.42-3.58-8-8-8H104c-4.42 0-8 3.58-8 8v16c0 4.42 3.58 8 8 8z"></path></svg>
<h4>
APPLICATIONS
</h4>
Cell type annotation, multi-omic data integration, biomarker and target identification.
<svg aria-hidden="true" viewBox="0 0 576 512" xmlns="http://www.w3.org/2000/svg"><path d="M208 0c-29.9 0-54.7 20.5-61.8 48.2-.8 0-1.4-.2-2.2-.2-35.3 0-64 28.7-64 64 0 4.8.6 9.5 1.7 14C52.5 138 32 166.6 32 200c0 12.6 3.2 24.3 8.3 34.9C16.3 248.7 0 274.3 0 304c0 33.3 20.4 61.9 49.4 73.9-.9 4.6-1.4 9.3-1.4 14.1 0 39.8 32.2 72 72 72 4.1 0 8.1-.5 12-1.2 9.6 28.5 36.2 49.2 68 49.2 39.8 0 72-32.2 72-72V64c0-35.3-28.7-64-64-64zm368 304c0-29.7-16.3-55.3-40.3-69.1 5.2-10.6 8.3-22.3 8.3-34.9 0-33.4-20.5-62-49.7-74 1-4.5 1.7-9.2 1.7-14 0-35.3-28.7-64-64-64-.8 0-1.5.2-2.2.2C422.7 20.5 397.9 0 368 0c-35.3 0-64 28.6-64 64v376c0 39.8 32.2 72 72 72 31.8 0 58.4-20.7 68-49.2 3.9.7 7.9 1.2 12 1.2 39.8 0 72-32.2 72-72 0-4.8-.5-9.5-1.4-14.1 29-12 49.4-40.6 49.4-73.9z"></path></svg>
<h4>
CORE MODELS
</h4>
Foundation models (scGPT, scContrast), Autoencoders, <br>AI-Agents for automated analysis
<svg aria-hidden="true" viewBox="0 0 512 512" xmlns="http://www.w3.org/2000/svg"><path d="M496 384H64V80c0-8.84-7.16-16-16-16H16C7.16 64 0 71.16 0 80v336c0 17.67 14.33 32 32 32h464c8.84 0 16-7.16 16-16v-32c0-8.84-7.16-16-16-16zM464 96H345.94c-21.38 0-32.09 25.85-16.97 40.97l32.4 32.4L288 242.75l-73.37-73.37c-12.5-12.5-32.76-12.5-45.25 0l-68.69 68.69c-6.25 6.25-6.25 16.38 0 22.63l22.62 22.62c6.25 6.25 16.38 6.25 22.63 0L192 237.25l73.37 73.37c12.5 12.5 32.76 12.5 45.25 0l96-96 32.4 32.4c15.12 15.12 40.97 4.41 40.97-16.97V112c.01-8.84-7.15-16-15.99-16z"></path></svg>
<h4>
VALUE
</h4>
Automated analysis and precise identification of cell populations and biomarkers
<img width="1024" height="572" src="https://evo-byte.com/wp-content/uploads/2026/05/single_cell-1024x572.webp" alt="" srcset="https://evo-byte.com/wp-content/uploads/2026/05/single_cell-1024x572.webp 1024w, https://evo-byte.com/wp-content/uploads/2026/05/single_cell-300x167.webp 300w, https://evo-byte.com/wp-content/uploads/2026/05/single_cell-768x429.webp 768w, https://evo-byte.com/wp-content/uploads/2026/05/single_cell.webp 1376w" sizes="(max-width: 1024px) 100vw, 1024px" />
<h2>Ready to Accelerate Your AI Journey?</h2>
We are looking forward to supporting your use case</p>
<img width="1022" height="1024" src="https://evo-byte.com/wp-content/uploads/2025/02/JoAl_Crop-1022x1024.webp" alt="image of contact person eof EVOBYTE" srcset="https://evo-byte.com/wp-content/uploads/2025/02/JoAl_Crop-1022x1024.webp 1022w, https://evo-byte.com/wp-content/uploads/2025/02/JoAl_Crop-300x300.webp 300w, https://evo-byte.com/wp-content/uploads/2025/02/JoAl_Crop-150x150.webp 150w, https://evo-byte.com/wp-content/uploads/2025/02/JoAl_Crop-768x769.webp 768w, https://evo-byte.com/wp-content/uploads/2025/02/JoAl_Crop.webp 1296w" sizes="(max-width: 1022px) 100vw, 1022px" />
<p><b>Dr. Jonathan Alles</b>
Your Point of Contact