AI-Driven Prediction of Protein and Antibody Developability for Biopharmaceutical Discovery
AI-Driven Developability Prediction for Protein and Antibody Engineering
Selecting the right therapeutic candidate requires more than optimizing biological activity. Proteins with excellent functional performance may still fail during manufacturing, formulation, storage, or scale-up because of poor developability characteristics.
Neoncorte Bio applies artificial intelligence, computational protein engineering, and machine learning to predict developability risks early in the discovery process, helping research teams prioritize candidates with a higher likelihood of successful development.
Our computational workflows support faster Design-Build-Test-Learn (DBTL) cycles by identifying potential liabilities before expensive laboratory and manufacturing efforts.
What Is Developability Prediction?
Developability prediction evaluates whether a protein or antibody is likely to progress successfully from discovery through manufacturing and commercialization.
Rather than focusing solely on biological activity, developability assessment considers multiple biophysical and manufacturing-related characteristics that influence downstream success.
Early computational prediction helps organizations:
Early computational prediction helps organizations:
- Prioritize stronger development candidates
- Reduce late-stage project failures
- Improve manufacturing readiness
- Support formulation development
- Reduce experimental workload
- Accelerate biologics development
- Improve R&D productivity
AI Technologies Behind Our Platform
Neoncorte Bio integrates advanced computational technologies including:
- Protein language models (PLMs)
- Artificial intelligence
- Machine learning
- Structure-informed protein modeling
- Computational mutagenesis
- Virtual Deep Mutational Scanning (DMS)
- Protein fitness landscape prediction
- Epistasis prediction
- Higher-order mutation prediction
- Active learning
- Bayesian optimization
- Multi-objective optimization
Developability Assessment Capabilities
Our computational platform supports prediction of:
- Protein StabilityEstimate sequence changes that may influence structural stability under development and manufacturing conditions.
- Protein SolubilityIdentify variants with improved solubility that may facilitate production, purification, and formulation.
- Aggregation RiskPredict sequence features associated with increased aggregation propensity.
- Expression YieldSupport selection of variants with improved recombinant protein expression potential.
- ManufacturabilityEvaluate protein characteristics relevant to scalable manufacturing and downstream processing.
- Sequence Liability PredictionIdentify sequence motifs that may contribute to degradation, chemical modification, or other development risks.
Properties Evaluated During Developability Prediction
Our AI workflows can assess:
- Protein stability
- Thermostability
- Melting temperature (Tm)
- Protein solubility
- Aggregation propensity
- Aggregation resistance
- Expression yield
- Manufacturability
- Sequence liabilities
- Binding affinity
- Structural integrity
- Oxidative stability
- Freeze-thaw stability
- pH stability
- Multi-objective optimization
Applications
-
Therapeutic Antibody Discovery
Prioritize antibody candidates with improved developability profiles before lead optimization. -
Therapeutic Protein Engineering
Support selection of proteins suitable for manufacturing, formulation, and commercialization. -
Biosimilar Development
Compare candidate molecules using computational developability assessment. -
Protein Drug Optimization
Improve molecular properties while maintaining biological function. -
Synthetic Biology
Evaluate engineered proteins intended for large-scale production.
Design-Build-Test-Learn (DBTL) Collaboration
Our computational workflow integrates prediction with laboratory validation.
Design
Analyze sequences, structures, and engineering objectives.
Build
Prioritize mutations and focused candidate libraries.
Test
Experimentally evaluate prioritized protein variants.
Learn
Continuously improve predictive models using newly generated experimental data.
Each DBTL cycle increases prediction accuracy while reducing unnecessary laboratory work.
Analyze sequences, structures, and engineering objectives.
Build
Prioritize mutations and focused candidate libraries.
Test
Experimentally evaluate prioritized protein variants.
Learn
Continuously improve predictive models using newly generated experimental data.
Each DBTL cycle increases prediction accuracy while reducing unnecessary laboratory work.
Why Choose Neoncorte Bio?
Neoncorte Bio combines expertise in:
- Artificial intelligence
- Machine learning
- Computational protein engineering
- Protein language models
- Structural biology
- Computational mutagenesis
- Virtual Deep Mutational Scanning
- Protein fitness landscape prediction
- Epistasis prediction
- Higher-order mutation prediction
- Active learning
- Bayesian optimization
- Multi-objective optimization
- Design-Build-Test-Learn (DBTL)
Who We Work With
Our platform supports:
- Biotechnology companies
- Pharmaceutical companies
- Antibody developers
- Biologics companies
- CROs and CDMOs
- Synthetic biology companies
- Industrial biotechnology organizations
- Academic research institutions
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 Candidate Selection with AI-Driven Developability Prediction
Contact Neoncorte Bio to discuss your developability prediction and protein 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.
-
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