AI-Driven Engineering of Regioselective Enzymes for Biocatalysis, Pharmaceutical Manufacturing, and Industrial Biotechnology

AI-Driven Regioselectivity Engineering

Many industrial and pharmaceutical reactions require enzymes that modify a specific position within a molecule while avoiding undesired reaction sites. Improving regioselectivity can increase product yield, simplify downstream purification, reduce waste, and improve manufacturing efficiency.

Neoncorte Bio combines artificial intelligence, computational protein engineering, and machine learning to engineer enzymes with enhanced regioselectivity while maintaining catalytic activity, stability, expression, and manufacturability.

Our AI-assisted workflows accelerate Design-Build-Test-Learn (DBTL) cycles by identifying promising variants before laboratory validation.

Why Engineer Regioselectivity?

Regioselective enzymes enable more efficient and sustainable manufacturing by directing catalytic activity toward the desired reaction site.
Engineering objectives include:
  • Improved regioselectivity
  • Increased product yield
  • Reduced side-product formation
  • Higher product purity
  • Improved reaction efficiency
  • Expanded substrate scope
  • Better industrial process performance
  • Reduced downstream purification requirements
  • More sustainable chemical synthesis
Computational prediction helps researchers prioritize mutations with the greatest likelihood of improving regioselective performance before experimental screening.

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
These AI-assisted approaches help identify promising enzyme variants while reducing unnecessary laboratory work.

Applications

Regioselectivity Engineering
  • Pharmaceutical Manufacturing

    Engineer enzymes for regioselective synthesis of active pharmaceutical ingredients (APIs) and complex intermediates.
  • Industrial Biocatalysis

    Develop enzymes that improve process efficiency while minimizing by-products.
  • Fine Chemical Manufacturing

    Increase reaction selectivity for specialty chemicals and advanced intermediates.
  • Agrochemical Production

    Optimize enzymes for selective synthesis of crop protection compounds.
  • Synthetic Biology

    Engineer metabolic pathways requiring highly controlled regioselective reactions.
Regioselectivity Engineering

Enzyme Classes We Support

Our AI platform supports engineering of:
  • Cytochrome P450 enzymes
  • Monooxygenases
  • Ketoreductases (KREDs)
  • Alcohol dehydrogenases (ADHs)
  • Transaminases
  • Lipases
  • Esterases
  • Laccases
  • Peroxidases
  • Oxidoreductases
  • Hydrolases
  • Transferases
  • Lyases
  • Isomerases

Properties Optimized Alongside Regioselectivity

Successful enzyme engineering requires balancing multiple molecular characteristics simultaneously.
  • Regioselectivity
  • Enantioselectivity
  • Stereoselectivity
  • Product selectivity
  • Substrate specificity
  • Catalytic activity
  • Catalytic efficiency (kcat/KM)
  • Protein stability
  • Thermostability
  • Melting temperature (Tm)
  • pH stability
  • Solvent tolerance
  • Oxidative stability
  • Freeze-thaw stability
  • Protein solubility
  • Expression yield
  • Manufacturability
  • Multi-objective optimization
Regioselectivity Engineering fitness landscape prediction

Design-Build-Test-Learn (DBTL) Integration

Our computational workflow integrates predictive modeling with laboratory validation.
Design
Analyze enzyme sequences, structures, substrate interactions, and reaction mechanisms.
Build
Prioritize mutations and focused variant libraries.
Test
Experimentally evaluate engineered enzyme variants.
Learn
Update predictive models using newly generated experimental data.

Each DBTL cycle improves prediction accuracy while reducing unnecessary experimental effort.
Regioselectivity Engineering design build test learn cycle

Why Choose Neoncorte Bio?

Neoncorte Bio combines expertise in:
Our AI-guided workflows help researchers engineer highly selective enzymes while complementing laboratory experimentation.

Who We Work With

Our solutions support:
  • Biotechnology companies
  • Pharmaceutical companies
  • Industrial enzyme manufacturers
  • Fine chemical manufacturers
  • Agrochemical companies
  • Synthetic biology startups
  • CROs and CDMOs
  • Academic research institutions
Frequently Asked Questions (FAQs)

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.
  • NVIDIA Inception Neoncorte Bio AI life sciences company
    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.
  • Nebius AI life sciences Neoncorte Bio
    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:
  • 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