AI-Driven Engineering of Therapeutic Antibodies for Reduced Solution Viscosity and Improved Developability
AI-Driven Antibody Viscosity Engineering
High solution viscosity is one of the most important developability challenges in therapeutic antibody development.
As antibody concentration increases, intermolecular interactions may significantly increase viscosity, complicating manufacturing, formulation, filling, and subcutaneous administration.
Neoncorte Bio applies AI-driven protein engineering to identify sequence modifications that may reduce antibody viscosity while maintaining key functional and developability characteristics.
Why Antibody Viscosity Matters
Many therapeutic antibodies are formulated at high concentrations to enable convenient dosing and reduce injection volume.
However, elevated viscosity can create challenges throughout product development, including:
- Difficult formulation
- Manufacturing complexity
- Filling and filtration challenges
- Reduced syringeability
- Limited subcutaneous delivery options
- Higher production costs
- Greater formulation risk
Application Areas
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High-Concentration Formulations
Optimize antibodies intended for concentrated formulations.
Benefit: Improved injectability and formulation flexibility. -
Subcutaneous Therapeutics
Reduce viscosity to support subcutaneous administration.
Benefit: Greater flexibility in product design and patient-friendly delivery options. -
Lead Optimization
Identify sequence modifications that improve developability while maintaining desired biological properties.
Benefit: Better candidate selection before advanced development. -
Manufacturing Process Development
Support antibodies intended for scalable commercial manufacturing.
Benefit: Reduced processing challenges and improved production efficiency. -
Biopharmaceutical Portfolio Optimization
Improve developability of existing antibody candidates.
Benefit: Enhanced manufacturability and lifecycle management.
Factors Contributing to High Viscosity
Antibody viscosity is influenced by multiple molecular properties, including:
- Surface charge distribution
- Electrostatic interactions
- Hydrophobic surface patches
- Self-association propensity
- Protein concentration
- Molecular flexibility
- Solution conditions
- Formulation composition
AI-Guided Antibody Viscosity Engineering
Neoncorte Bio combines computational protein engineering, structural biology, and machine learning to evaluate molecular features associated with antibody viscosity.
Our engineering workflow may incorporate:
- Antibody sequence analysis
- Structure-informed modeling
- Surface electrostatic analysis
- Hydrophobicity assessment
- Self-association prediction
- Aggregation propensity analysis
- Protein language models
- Machine learning
- Multi-objective optimization
- Design-Build-Test-Learn (DBTL) methodologies
- Candidate sequence modifications are prioritized for experimental validation based on project-specific objectives.
Engineering Objectives
Viscosity engineering is often performed together with optimization of:
- Target binding affinity
- Specificity
- Aggregation resistance
- Solubility
- Thermal stability
- Freeze-thaw stability
- Recombinant expression
- Manufacturability
- Formulation compatibility
- Overall developability
- This multi-parameter approach helps improve formulation properties while preserving therapeutic performance.
Supported Antibody Formats
Our workflows support developability optimization for:
- Monoclonal antibodies (mAbs)
- Bispecific antibodies
- Antibody-drug conjugates (ADCs)
- Fab fragments
- scFv fragments
- Single-domain antibodies (VHH/Nanobodies)
- Fc-fusion proteins
- Engineered antibody variants
Design-Build-Test-Learn (DBTL) Integration
Viscosity engineering benefits from iterative computational prediction and laboratory validation.
Neoncorte Bio supports:
- Antibody sequence analysis
- AI-guided viscosity assessment
- Variant prioritization
- Antibody engineering
- Experimental characterization
- Machine learning model refinement
- Successive Design-Build-Test-Learn (DBTL) cycles
What Neoncorte Bio Delivers
- AI-guided antibody viscosity engineering
- Sequence-based viscosity assessment
- Structure-informed antibody optimization
- Self-association analysis
- Aggregation propensity assessment
- Multi-parameter developability optimization
- Design-Build-Test-Learn (DBTL) workflows
- Confidential B2B antibody engineering partnerships
Who We Work With
- Biopharmaceutical companies
- Therapeutic antibody developers
- Biotechnology startups
- Antibody discovery companies
- Pharmaceutical companies
- CDMOs and CROs
- Research organizations
- Biologics platform developers
Frequently Asked Questions (FAQs)
Our platform harnesses advanced artificial intelligence and machine learning to design, optimize, and engineer proteins tailored to your specific requirements. Whether you need improved enzyme efficiency, novel protein functions, or tailored biotherapeutics, our solutions streamline the protein design process and reduce time-to-market.
Our services are ideal for biotechnology and pharmaceutical companies, academic research labs, industrial manufacturers, and any organization involved in protein-based product development. Applications include enzyme optimization, biocatalyst development, therapeutic protein design, and more.
The process begins with your input data, such as protein sequences, structural information, and desired functional attributes. Our AI algorithms then generate a library of potential protein variants and use predictive modeling to evaluate their stability, activity, and specificity. The top candidates are ranked and provided for further experimental validation, thereby reducing the number of required lab iterations.
Our platform is versatile and can be applied to a wide range of proteins, including enzymes, structural proteins, and therapeutic proteins. We can optimize existing proteins or design novel ones for applications in catalysis, biopharmaceuticals, diagnostics, and more.
Absolutely. We work closely with you to tailor our AI models and algorithms to your target proteins and desired outcomes. This includes integrating proprietary data and fine-tuning parameters to ensure that our platform delivers the most relevant and high-performing protein candidates for your application.
Our platform offers robust API support and standard data export formats (e.g., CSV, JSON) to seamlessly integrate with your Laboratory Information Management Systems (LIMS) and other data analytics pipelines. We also provide comprehensive onboarding and training to ensure a smooth adoption process.
Our models are trained on extensive datasets of protein interactions, structural data, and experimental results. We continuously refine our algorithms with real-world feedback to improve prediction accuracy. While our computational predictions are highly reliable, we recommend subsequent in vitro or in vivo validation to confirm the performance of the engineered proteins.
Yes, we provide collaborative support throughout the validation process. Our team can help design proof-of-concept studies and coordinate with your wet lab partners to verify the performance of our predicted protein variants.
We offer flexible pricing models based on the scope and duration of your project. Options include subscription-based plans for ongoing R&D, as well as project-specific pricing for one-off protein engineering campaigns. Please contact us for a personalized consultation and detailed quote.
Our dedicated customer support team is available to assist you at every stage—from initial onboarding and technical training to troubleshooting and ongoing optimization. We offer support via email, phone, and live chat, along with detailed documentation and a comprehensive FAQ section.
Improve Antibody Developability Through Viscosity Engineering
Contact us to discuss your antibody viscosity engineering project
Neoncorte Bio
Where AI Meets Biotechnology
Neoncorte Bio is at the forefront of the convergence between artificial intelligence and enzyme engineering. Our team comprises experts in computational biology, bioinformatics, and machine learning, all driven by a mission to accelerate innovation in enzyme design. By leveraging our advanced AI models, we provide unparalleled solutions that enhance efficiency, reduce costs, and push the boundaries of what's possible in enzyme engineering
Proud Member of Leading Global AI Programs
Neoncorte Bio is part of the NVIDIA Inception and Nebius for Startups programs — two of the world’s leading ecosystems for high-performance AI innovation. These partnerships strengthen our ability to deliver next-generation AI-driven protein, enzyme, and aptamer engineering.
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As a member of NVIDIA Inception, Neoncorte Bio gains access to cutting-edge GPU technologies, expert guidance, and a global AI ecosystem that supports companies from prototype to production. The program empowers us to explore new AI opportunities and build high-performance biological design pipelines powered by NVIDIA’s world-class platform. -
Through Nebius for Startups, we gain access to high-performance compute infrastructure optimized for large-scale AI workloads, along with hands-on technical guidance and a strong community of innovative AI companies. Nebius enables us to train and deploy complex biological models more efficiently — accelerating enzyme, protein, and aptamer design while supporting rapid scaling of our R&D pipelines.
Publications
Scientific Publication of Neoncorte Bio Team
- Modification of natural enzymes to introduce new properties and enhance existing ones is a central challenge in bioengineering. This study is focused on the development of Taq polymerase mutants that show enhanced reverse transcriptase (RTase) activity while retaining other desirable properties such as fidelity, 5′-3′ exonuclease activity, effective deoxyuracil incorporation, and tolerance to locked nucleic acid (LNA)-containing substrates.
- The transcriptomic data are being frequently used in the research of biomarker genes of different diseases and biological states. The most common tasks there are the data harmonization and treatment outcome prediction. Both of them can be addressed via the style transfer approach. Either technical factors or any biological details about the samples which we would like to control (gender, biological state, treatment, etc.) can be used as style components.
- List of all Neoncorte Bio publications dedicated to Molecular Biology, Biotechnology, Artificial Intelligence and Artificial Neural Networks, published mostly by Nikolay Russkikh, CEO of Neoncorte Bio
Our Expertise in Action
With extensive experience in AI applications and software engineering tailored to the life sciences, we specialize in solving complex challenges and delivering innovative solutions for our customers. Our work demonstrates a deep understanding of cutting-edge technologies and their application in the real world.
Here are examples of the types of projects we have successfully delivered:
Here are examples of the types of projects we have successfully delivered:
- Automated NGS Data Analysis:Designed a production-grade solution for the automated processing, annotation, and analysis of Next-Generation Sequencing (NGS) data.
- Single-Cell Data Integration:Built state-of-the-art tools for integrating multimodal single-cell data, achieving recognition for technical excellence.
- Metagenomic Classification Algorithms:Developed advanced methods for classifying sequencing reads in metagenomics research.
- High-Throughput Image Processing Pipelines:Engineered an efficient pipeline to process millions of sequencing images with exceptional accuracy.
- Cell Counting via AI:Created a computer vision solution for precise cell counting in microphotography images, streamlining data analysis.
Get in touch with our team
Phone: +1-503-754-3958
Email: contact@neoncorte.com
Email: contact@neoncorte.com
© All Rights Reserved. Neoncorte Bio. 2024-2026
contact@neoncorte.com
contact@neoncorte.com