Generative AI for Enzyme Engineering

Generate and screen millions of enzyme variants in silico in weeks, not years. Our AI-driven design-build-test-learn cycles compress R&D timelines, enabling you to bring novel biocatalysts to pilot scale 5-10x faster than traditional methods.

AI-optimized enzymes achieve superior activity, specificity, and stability under industrial conditions. We target precise metrics like kcat/Km improvement, thermal stability at >70°C, and tolerance to non-aqueous solvents, ensuring performance in your specific process.

Drastically lower wet-lab experimentation costs by prioritizing only the most promising AI-designed variants for synthesis and testing. Our platform minimizes failed experiments and reagent waste, delivering a higher return on your R&D investment.

Design enzymes for greener chemistry—replace harsh chemical catalysts, operate at ambient temperature and pressure, and utilize renewable feedstocks. Achieve ESG goals and reduce environmental impact while improving process economics.

Create novel, patentable enzyme sequences with no homology to existing patented variants. Our generative models explore uncharted regions of protein sequence space, securing your competitive advantage and ensuring commercial freedom to operate.

Deploy a reusable AI platform for continuous enzyme optimization across your product pipeline. The underlying models and workflows are adaptable to new substrates, reactions, and host organisms, providing long-term strategic value beyond a single project.

We train custom graph neural networks and protein language models (e.g., ESM-2, AlphaFold) on your proprietary enzyme data and public corpora. This creates a foundational understanding of sequence-to-activity relationships specific to your industrial process.

Outcome: Higher accuracy in-silico screening, reducing wet-lab validation cycles by 70%.

Our AI doesn't just predict—it designs. Using diffusion models and evolutionary algorithms, we generate novel enzyme variants optimized for your specific targets: catalytic efficiency (kcat/KM), thermostability, pH tolerance, and non-natural substrate activity.

Outcome: A diverse, high-potential design library of 10,000+ variants, pre-filtered for synthesizability.

Every generated variant undergoes rigorous computational analysis. We predict folding stability (ΔΔG), aggregation propensity, and structural dynamics using molecular dynamics simulations to filter out non-viable designs before synthesis.

Outcome: >90% of AI-designed variants express solubly and maintain structural integrity, minimizing costly failed expressions.

We partner with your lab or our CRO network to express, purify, and characterize top candidate variants. Activity assays (e.g., HPLC, spectrophotometry) provide ground-truth data that is fed back to retrain and refine our AI models in an iterative loop.

Outcome: Continuous model improvement; each iteration increases the hit rate of improved variants by 15-30%.

We engineer robust data and model pipelines. This includes version-controlled training datasets, containerized model inference APIs, and automated reporting to ensure your team can reliably reproduce results and deploy models into your R&D workflow.

Outcome: Fully documented, audit-ready AI workflows compliant with internal QA and external regulatory standards.

We structure engagements with clear, technical success criteria tied to lab results. Projects are phased with go/no-go gates based on achieving predefined improvements in catalytic activity or stability, de-risking your investment.

Outcome: Predictable project timelines and budgets, with deliverables tied to measurable biochemical performance gains.