AI-Driven Enzyme Engineering to Maximize Biocatalytic Process Productivity
AI-Driven Space-Time Yield (STY) Optimization
Space-time yield (STY) is one of the most important performance metrics in industrial biocatalysis. It measures how much product can be generated per unit reactor volume over time and directly influences manufacturing capacity, production costs, and process economics.
Neoncorte Bio applies artificial intelligence, computational enzyme engineering, and machine learning to help improve enzyme performance and increase space-time yield by optimizing multiple protein properties simultaneously.
Rather than optimizing a single characteristic in isolation, our AI-guided workflows identify variants that support higher overall process productivity while accelerating iterative Design-Build-Test-Learn (DBTL) cycles.
Why Space-Time Yield Matters
Improving STY enables manufacturers to produce more product using the same equipment, reducing production costs while increasing manufacturing efficiency.
Higher space-time yield can contribute to:
- Increased reactor productivity
- Higher product output
- Lower manufacturing costs
- Reduced process time
- Improved process economics
- More efficient use of production equipment
- Higher enzyme productivity
- Better commercial scalability
AI Technologies Behind Our Platform
Neoncorte Bio integrates advanced computational technologies including:
- Protein language models (PLMs)
- Artificial intelligence
- Machine learning
- Structure-informed enzyme 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
Engineering for Higher Space-Time Yield
Improving STY typically requires simultaneous optimization of multiple enzyme characteristics rather than focusing on a single parameter.
Our computational workflows support optimization of:
Our computational workflows support optimization of:
- Catalytic EfficiencyIncrease reaction rates through improved catalytic performance.
- Enzyme StabilityImprove operational lifetime under manufacturing conditions.
- ThermostabilityEnable operation at elevated temperatures that increase reaction productivity.
- Protein SolubilityImprove enzyme concentration and manufacturing performance.
- Expression YieldIncrease recombinant enzyme production while lowering manufacturing costs.
- Substrate SpecificityImprove catalytic efficiency toward desired substrates.
- Product SelectivityReduce by-product formation while increasing process efficiency.
- Solvent and Process StabilityEnhance enzyme performance under industrial operating conditions.
Applications
Our STY optimization workflows support:
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Pharmaceutical Manufacturing
Increase productivity of enzymatic API synthesis. -
Industrial Biocatalysis
Improve enzyme performance for large-scale manufacturing. -
Fine Chemical Production
Optimize catalytic processes for higher throughput and improved economics. -
Food & Beverage Manufacturing
Increase efficiency of industrial enzyme processes. -
Bio-Based Chemicals
Improve enzyme productivity for sustainable manufacturing.
Enzyme Classes We Support
Our AI platform supports engineering of:
- Alcohol dehydrogenases
- Ketoreductases (KREDs)
- Transaminases
- Monooxygenases
- Cytochrome P450 enzymes
- Lipases
- Esterases
- Proteases
- Amylases
- Cellulases
- Xylanases
- Laccases
- Peroxidases
- Glucose oxidases
- Nitrilases
- Oxidoreductases
- Hydrolases
- Transferases
Design-Build-Test-Learn (DBTL) Collaboration
Our computational workflow integrates predictive modeling with laboratory validation.
Design
Analyze enzyme sequences, structures, and process objectives.
Build
Prioritize mutations and focused variant libraries.
Test
Experimentally evaluate engineered enzymes under process-relevant conditions.
Learn
Continuously improve predictive models using newly generated experimental data.
Each DBTL cycle increases engineering efficiency while reducing unnecessary experimental screening.
Analyze enzyme sequences, structures, and process objectives.
Build
Prioritize mutations and focused variant libraries.
Test
Experimentally evaluate engineered enzymes under process-relevant conditions.
Learn
Continuously improve predictive models using newly generated experimental data.
Each DBTL cycle increases engineering efficiency while reducing unnecessary experimental screening.
Why Choose Neoncorte Bio?
Neoncorte Bio combines expertise in:
- Artificial intelligence
- Machine learning
- Computational enzyme 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:
- Industrial biotechnology companies
- Pharmaceutical manufacturers
- Industrial enzyme producers
- Fine chemical manufacturers
- Food ingredient companies
- Bio-based chemical manufacturers
- Synthetic biology companies
- CROs and CDMOs
Frequently Asked Questions (FAQs)
Our solutions leverage advanced artificial intelligence and machine learning to design, optimize, and engineer enzymes tailored to your specific applications. Whether you need enhanced catalytic efficiency, improved stability, or novel enzyme functionalities, our platform streamlines the design process, reduces experimental iterations, and accelerates time-to-market.
Our services are ideal for biotechnology and pharmaceutical companies, industrial manufacturers, academic research labs, and any organization involved in enzyme applications. Industries such as biofuels, food processing, personal care, paper and pulp, and environmental management can all benefit from our AI-driven enzyme engineering tools.
Our process begins with your input data—such as enzyme sequences, structural information, and target reaction profiles. Our proprietary AI algorithms generate a library of enzyme variants and use predictive modeling to evaluate their catalytic activity, stability, and specificity. The top-performing candidates are ranked and provided for further experimental validation, significantly reducing the number of lab iterations needed.
Our platform is versatile and can be applied to a wide range of enzymes including hydrolases, oxidoreductases, transferases, and lyases. We focus on both enhancing the performance of existing enzymes and designing entirely new biocatalysts for applications in industrial processes, therapeutics, and research.
Absolutely. We collaborate with you to tailor our AI models and parameters to meet your project’s unique requirements. This includes integrating your proprietary data, adjusting target properties, and aligning the enzyme design with your process conditions to deliver a highly customized solution.
Our software features robust API support and data export options (e.g., CSV, JSON) that enable seamless integration with Laboratory Information Management Systems (LIMS) and other data analytics pipelines. We also offer comprehensive onboarding, training, and technical support to ensure smooth adoption by your team.
Our models are trained on extensive datasets from scientific literature and experimental data. Continuous feedback from laboratory validations helps refine our predictions. Although our AI predictions are highly reliable, we recommend in vitro or in vivo validation to confirm the performance of the engineered enzymes under your specific conditions.
Yes, we provide collaborative support during the validation phase. Our team can assist in designing proof-of-concept studies, advising on experimental protocols, and interpreting validation results to ensure that the engineered enzymes meet your performance criteria.
Our pricing is flexible and depends on the scope, duration, and level of customization required for your project. We offer subscription-based models for ongoing research and project-based pricing for specific campaigns. Please contact us for a personalized consultation and detailed quote.
Our dedicated customer support team is available throughout your journey—from initial consultation and onboarding to ongoing technical support and regular software updates. We offer multiple support channels including email, phone, live chat, and detailed documentation to ensure your success.
Maximize Space-Time Yield with AI-Driven Enzyme Engineering
Contact Neoncorte Bio to discuss your space-time yield (STY) optimization 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