AI-Guided Experimental Design to Accelerate Protein and Enzyme Engineering
Accelerate Protein Engineering with Active Learning and Bayesian Optimization
Protein engineering often requires selecting which variants to build and test from an enormous sequence space.
Testing every possible mutation is impossible, while random selection can waste valuable laboratory resources.
Neoncorte Bio applies active learning and Bayesian optimization to identify the most informative protein variants for experimental evaluation, helping organizations accelerate optimization while reducing unnecessary screening.
Why AI-Guided Experimental Design Matters
Traditional engineering programs often involve:
- Large mutation libraries
- Extensive laboratory screening
- Multiple Design-Build-Test-Learn (DBTL) cycles
- High development costs
- Long optimization timelines
- Limited experimental capacity
What Is Active Learning?
Active learning is a machine learning strategy in which the model iteratively recommends the next experiments expected to provide the most useful information.
Instead of evaluating protein variants at random, active learning prioritizes candidates that can:
- Improve predictive model performance
- Reduce uncertainty in unexplored regions of sequence space
- Accelerate discovery of high-performing variants
- Support efficient iterative optimization
What Is Bayesian Optimization?
Bayesian optimization is a statistical optimization framework designed for problems where experiments are expensive and data are limited.
In protein engineering, Bayesian optimization helps balance two complementary goals:
- Exploration: investigate promising but uncertain regions of protein sequence space.
- Exploitation: refine variants already showing favorable performance.
Application Areas
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Industrial Enzyme Engineering
Optimize industrial enzymes while reducing laboratory screening requirements.
Benefit: Faster engineering campaigns with more efficient use of experimental resources. -
Pharmaceutical Protein Development
Support optimization of therapeutic proteins and biocatalysts during lead development.
Benefit: Better candidate prioritization and improved decision-making. -
Synthetic Biology
Accelerate engineering of proteins used in biological platforms and engineered organisms.
Benefit: Faster iterative development and improved model performance. -
Specialty Chemical Biocatalysis
Develop enzymes for selective industrial synthesis through AI-guided optimization.
Benefit: More efficient identification of commercially valuable variants. -
Research & Collaborative Programs
Support computational protein engineering projects that combine predictive modeling with laboratory validation.
Benefit: Higher information gain from each experimental cycle.
AI-Guided DBTL Workflow
Neoncorte Bio integrates active learning and Bayesian optimization within iterative Design-Build-Test-Learn workflows.
Typical workflow:
- Define engineering objectives
- Analyze sequence and structural information
- Build predictive machine learning models
- Prioritize variants using active learning or Bayesian optimization
- Experimentally evaluate selected candidates
- Retrain models with new experimental data
- Repeat optimization cycles until project goals are achieved
Engineering Objectives
Active learning and Bayesian optimization can support optimization of:
- Catalytic activity
- Thermostability
- pH stability
- Solvent tolerance
- Oxidative stability
- Recombinant expression
- Solubility
- Aggregation resistance
- Substrate specificity
- Enantioselectivity
- Operational stability
- Manufacturability
Why Choose AI-Guided Optimization?
Compared with conventional experimental planning, active learning and Bayesian optimization can help:
- Prioritize informative experiments
- Reduce unnecessary laboratory work
- Improve model accuracy over time
- Make better use of limited experimental data
- Accelerate iterative optimization
- Support multi-objective protein engineering
What Neoncorte Bio Delivers
- Active learning workflows for protein engineering
- Bayesian optimization strategies
- AI-guided experimental design
- Protein sequence analysis
- Candidate prioritization
- Multi-objective optimization support
- Design-Build-Test-Learn (DBTL) integration
- Confidential B2B protein engineering partnerships
Who We Work With
- Industrial biotechnology companies
- Pharmaceutical developers
- Enzyme manufacturers
- Synthetic biology startups
- CDMOs and CROs
- Agricultural biotechnology companies
- Food ingredient manufacturers
- Research organizations
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.
Make Every Experiment Count
Contact us to discuss your AI-guided 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.
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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