AI-Driven Enzyme Engineering Solutions

inquiry

Accelerating enzyme discovery, optimization, and biocatalyst development through AI-assisted design and data-driven engineering.

Why AI for Enzyme Engineering?

Traditional enzyme engineering faces fundamental challenges that limit the pace and scale of discovery and optimization.

Low Screening Efficiency

Traditional methods screen only a tiny fraction of possible enzyme variants, leaving vast sequence space unexplored.

Massive Sequence Space

Sequence combinations exceed experimental capacity by orders of magnitude, making exhaustive search impractical.

Limited Structure-Function Insight

Understanding sequence-to-function relationships remains a major challenge for rational enzyme design.

Long Optimization Cycles

Iterative wet-lab optimization takes months to achieve desired results, slowing time-to-market.

Our AI Technology Ecosystem

Not just algorithms—a complete toolkit spanning sequence intelligence to wet lab validation.

AI-Guided Protein Intelligence Platform

A unified computational engine that integrates sequence analysis, structural modeling, ML-driven ranking, and wet lab feedback into a single intelligent system for end-to-end enzyme engineering.

Sequence Analysis

AI-driven sequence analysis, alignment, and feature extraction

Structure Modeling

3D enzyme structure prediction and binding pocket analysis

ML Ranking

Gaussian process and neural network-based fitness prediction

Wet Lab Integration

Active learning with experimental feedback loops

Solution Landscape

Four interconnected solution domains covering the full enzyme engineering pipeline.

Solution	Description	Price
AI-Guided Enzyme Discovery	AI-guided mining of novel enzymes from sequence databases and metagenomic libraries. Includes activity prediction and novel fold detection.	Inquiry
AI-Guided Enzyme Optimization	Targeted property enhancement using ML-guided mutation strategies for thermostability, substrate scope, and enantioselectivity.
AI-Assisted Directed Evolution	Accelerated evolution workflows with intelligent library design and high-throughput screening integration.
Generative AI Enzyme Design	De novo enzyme creation for non-natural reactions and substrates using generative AI models.

AI-Integrated Workflow

AI-integrated enzyme engineering workflow

Application Areas

AI enzyme engineering delivers impact across diverse industries.

Pharmaceuticals

Enzyme-driven API synthesis, chiral intermediate production, and biocatalytic route optimization.

Industrial Enzymes

Process optimization for bulk enzyme manufacturing and cost reduction.

Food & Beverage

Novel food-grade enzymes for improved processing and product quality.

Synthetic Biology

Biosensor development and metabolic pathway engineering.

Green Chemistry

Sustainable biocatalytic process development with reduced environmental impact.

Diagnostics

High-performance enzymes for diagnostic assays and detection systems.

Featured AI Case Examples

Reac-Discovery: AI-Driven Catalytic Reactor Design Platform

Novel Hydrolase Discovery via AI-Guided Sequence Mining Figure 1. Reac-Discovery: an artificial intelligence–driven platform for continuous-flow catalytic reactor discovery and optimization. (Tinajero et al., 2025)

Reac-Discovery is an integrated digital platform that combines artificial intelligence, mathematical modeling, 3D printing, and self-driving experimentation to accelerate catalytic reactor development. The system includes three interconnected modules: Reac-Gen for parametric reactor design using periodic open-cell structures (POCs), Reac-Fab for high-resolution reactor fabrication and catalyst functionalization, and Reac-Eval for automated multi-reactor testing with real-time NMR monitoring and machine learning optimization. By controlling topology parameters such as size, level, and resolution, the platform generates highly tunable reactor geometries and detailed structural descriptors. Case studies in acetophenone hydrogenation and CO₂ cycloaddition demonstrated exceptional catalytic performance, including record-high space–time yield for triphasic CO₂ conversion using immobilized catalysts.

Engineered FPOX for Selective HbA1c Detection

Thermostable Lipase Engineering via ML-Directed Evolution Figure 2. Schematic diagram of interacting of the wild-type FPOX with Fru-ValHis (a) and Fru-Lys (b). (Shahbazmohammadi et al., 2019)

This study engineered fructosyl peptide oxidase (FPOX), a diagnostic enzyme used in diabetes testing, to improve the selective detection of hemoglobin A1c (HbA1c). Using structural analysis, molecular modeling, and mutagenesis, researchers identified key amino acid residues affecting substrate specificity. The Tyr261Trp mutant showed significantly enhanced preference for Fru-ValHis, an HbA1c model substrate, while greatly reducing activity toward Fru-Lys, which can interfere with measurements. Experimental characterization revealed a 5.1-fold increase in specific activity and an 11.7-fold improvement in catalytic efficiency for Fru-ValHis, alongside substantial decreases for Fru-Lys. These results demonstrate that the engineered FPOX variant is a promising candidate for more accurate HbA1c determination in blood diagnostics.

FAQs

Q: What AI methods do you use for enzyme engineering?

A: We leverage sequence embeddings, AlphaFold structure prediction, Gaussian process optimization, variational autoencoders, and transformer-based generative models. The specific approach depends on your project goals—discovery, optimization, or de novo design.
Q: Do I need existing enzyme data to start?

A: Not necessarily. For discovery projects, we mine public databases. For optimization, even a small dataset (20-50 variants) is sufficient. We can also generate training data through wet-lab screening.
Q: How does AI compare to traditional directed evolution?

A: AI-guided approaches typically reduce experimental rounds by 3-5x. ML models intelligently suggest mutations most likely to improve target properties, rather than relying on random mutagenesis.
Q: What deliverables do I receive?

A: Prioritized mutation lists or designed sequences, model performance reports, structural analysis, experimental validation data, and a comprehensive project report with recommendations.
Q: How long does a typical AI enzyme project take?

A: Prediction-only projects: 2-4 weeks. Optimization with wet-lab validation: 2-3 months. Full discovery-to-optimization programs: 4-6 months.
Q: Can you handle proprietary enzyme sequences?

A: Absolutely. We operate under strict confidentiality agreements. All proprietary data is handled in secure environments and never shared outside your project scope.

References:

Tinajero C, Zanatta M, Sánchez-Velandia JE, García-Verdugo E, Sans V. Reac-Discovery: an artificial intelligence–driven platform for continuous-flow catalytic reactor discovery and optimization. Nat Commun. 2025;16(1):9062. doi:10.1038/s41467-025-64127-1
Shahbazmohammadi H, Sardari S, Lari A, Omidinia E. Engineering an efficient mutant of Eupenicillium terrenum fructosyl peptide oxidase for the specific determination of hemoglobin A1c. Appl Microbiol Biotechnol. 2019;103(4):1725-1735. doi:10.1007/s00253-018-9529-9

First Name:

Last Name:

Email *

Phone Number:

Company/Institution:

Country or Region:

Quantity:

Services & Products of Interested *

Project Description:

For research and industrial use only. Not intended for personal medicinal use. Certain food-grade products are suitable for formulation development in food and related applications.

Services

Online Inquiry