Accelerate Protein Engineering with Artificial Intelligence and Machine Learning
AI Protein Design for Next-Generation Protein Engineering
Artificial intelligence is transforming protein engineering by enabling researchers to explore vast protein sequence spaces more efficiently than traditional trial-and-error approaches. Instead of experimentally testing thousands of variants, AI-guided protein design helps prioritize promising candidates before laboratory validation.
Neoncorte Bio combines artificial intelligence, machine learning, protein language models, and computational biology to support the design and optimization of enzymes, antibodies, therapeutic proteins, and other biologics.
Our AI-driven workflows complement laboratory experimentation and help accelerate Design-Build-Test-Learn (DBTL) cycles.
What Is AI Protein Design?
AI protein design applies machine learning algorithms and computational biology to predict how amino acid substitutions may influence protein function and developability.
Rather than relying solely on large-scale experimental screening, AI-assisted methods help researchers:
- Prioritize beneficial mutations
- Explore larger sequence spaces
- Reduce unnecessary laboratory experiments
- Optimize multiple protein properties simultaneously
- Improve engineering efficiency
- Support informed decision-making throughout R&D
AI Technologies Behind Our Platform
Neoncorte Bio integrates modern computational technologies, including:
- Protein language models (PLMs)
- Machine learning
- Artificial intelligence
- 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
AI Protein Design Capabilities
Our computational workflows support:
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Rational Mutation PrioritizationPredict amino acid substitutions likely to improve target protein properties. -
Computational MutagenesisEvaluate the predicted impact of individual mutations and mutation combinations before laboratory experiments. -
Virtual Deep Mutational ScanningAnalyze millions of potential sequence variants computationally. -
Protein Fitness Landscape AnalysisModel sequence-function relationships to identify productive engineering trajectories. -
Smart Library DesignGenerate focused mutation libraries that reduce screening while maintaining sequence diversity. -
Multi-Parameter OptimizationOptimize several protein properties simultaneously instead of improving one characteristic at a time.
Engineering Objectives
AI protein design can support optimization of:
- Protein stability
- Thermostability
- Melting temperature (Tm)
- Catalytic activity
- Catalytic efficiency
- Binding affinity
- Protein developability
- Manufacturability
- Expression yield
- Aggregation resistance
- Protein solubility
- pH stability
- Solvent tolerance
- Oxidative stability
- Freeze-thaw stability
- Cofactor specificity
- Substrate specificity
- Enantioselectivity
- Antibody viscosity
- Multi-parameter optimization
Protein Types Supported
Our AI protein design platform can be applied to:
- Industrial enzymes
- Therapeutic proteins
- Monoclonal antibodies
- Antibody fragments
- Cytokines
- Growth factors
- Diagnostic proteins
- Biosensor proteins
- Receptor proteins
- Synthetic biology proteins
- Novel protein scaffolds
- Fusion proteins
- Engineered enzymes
Industries We Support
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Biopharmaceutical Development
Design and optimize therapeutic proteins, biologics, and antibody candidates. -
Industrial Biotechnology
Engineer enzymes and proteins for sustainable manufacturing and biocatalysis. -
Synthetic Biology
Improve proteins used in engineered metabolic pathways and microbial production systems. -
Diagnostics
Optimize proteins for molecular diagnostics and biosensor applications. -
Environmental Biotechnology
Develop proteins for biodegradation, recycling, and environmental remediation.
Design-Build-Test-Learn (DBTL) Collaboration
Our AI protein design workflow supports every phase of iterative protein engineering.
Design
Analyze protein sequences, structures, and engineering goals.
Build
Prioritize mutations and focused variant libraries.
Test
Experimentally evaluate engineered proteins.
Learn
Use experimental data to continuously improve predictive models.
Each DBTL cycle refines computational predictions and accelerates future engineering campaigns.
Analyze protein sequences, structures, and engineering goals.
Build
Prioritize mutations and focused variant libraries.
Test
Experimentally evaluate engineered proteins.
Learn
Use experimental data to continuously improve predictive models.
Each DBTL cycle refines computational predictions and accelerates future engineering campaigns.
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 Uses AI Protein Design?
Our platform supports:
- Biotechnology companies
- Pharmaceutical companies
- Industrial enzyme manufacturers
- Antibody developers
- Synthetic biology startups
- Diagnostic companies
- CROs and CDMOs
- Academic research laboratories
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.
Accelerate AI Protein Design
Contact Neoncorte Bio to discover how AI protein design can accelerate your protein engineering program
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