Our integrated AI platform powers four complementary biotechnology service directions — enzyme design, protein design, strain engineering, and bioinformatics — all validated through our high-throughput wet lab infrastructure to deliver results you can trust.
AI predictions alone are not enough. A model might score a variant favorably in silico, only for it to fail at the bench — wrong expression host, poor solubility, unexpected substrate competition, or instability under process conditions. The gap between prediction and performance is where projects stall.
Our platform is built to close that gap. Every AI design we produce flows through an integrated wet lab validation pipeline, and the resulting experimental data feeds directly back into our models. This closed-loop approach means each iteration is sharper than the last.
Experimental results feed directly back into the computational pipeline, enabling rapid design–test–iterate cycles with measurable improvement each round.
Enzyme design, protein design, strain engineering, and bioinformatics are not siloed — data and insights flow across all four domains for multi-scale solutions.
Use a single service direction or combine all four. Project scope, deliverables, and reporting are tailored to your timeline and budget requirements.
Explore our comprehensive suite of AI-powered biotechnology services, each backed by cutting-edge computational models and validated through our wet lab infrastructure
Leverage state-of-the-art AI models to optimize enzyme activity, stability, substrate specificity, and catalytic efficiency. From existing enzyme improvement to de novo active site engineering, our platform covers all enzyme engineering needs across all six EC classes.
Create entirely novel functional proteins from scratch or redesign existing scaffolds with precision. From de novo protein backbone generation and active site embedding to antibody humanization and protein-protein interface design, our AI platform unlocks previously inaccessible design spaces.
Design and optimize high-performance microbial strains for sustainable production of target compounds. From metabolic pathway prediction and flux analysis to chassis cell selection and genome-scale engineering, our platform accelerates strain development from concept to production.
Transform raw omics data into actionable biological insights using our AI-powered bioinformatics platform. From multi-omics integration and comparative genomics to differential expression analysis and custom pipeline development, we deliver comprehensive computational biology support.
A vertically integrated computational infrastructure that spans from structural biology to generative AI and high-throughput experimental validation
Enginoma Structure and Enginoma Complex provide accurate 3D protein structure predictions, enabling precise active site analysis, pocket identification, and structure-guided engineering. Combined with molecular dynamics simulations for functional validation.
Enginoma protein language models and proprietary performance-calibrated sequence engines trained on proprietary enzyme-function datasets enable zero-shot variant scoring, sequence-function mapping, and hallucination-based design without experimental data.
Our automated wet lab platform — including microtiter screening, thermal shift assays, kinetic characterization, and next-generation sequencing — validates AI predictions at scale. Validated data feeds back into model training to create a continuous improvement loop.
A systematic four-step approach that transforms your biological challenge into a validated, production-ready solution
Define project objectives, gather sequence/structure data, establish success criteria, and design screening assays aligned with your application requirements.
Deploy structure prediction models, protein language models, and generative AI to produce ranked design candidates with predicted functional properties.
Express, purify, and characterize AI-designed candidates using high-throughput screening, thermal assays, and kinetic measurements.
Iterate design based on experimental feedback. Deliver lead candidates with full characterization reports and scale-up support as needed.
"Integrating their AI-driven enzyme optimization into our development pipeline reduced our enzyme engineering timeline from 18 months to under 6 — the data flywheel concept truly works in practice."
"Their de novo protein design capabilities are unmatched. They engineered a completely novel scaffold for our target application — something we had given up on after trying three other vendors."
"The combination of AI-driven strain engineering with their fermentation optimization team delivered a titer improvement of 8-fold compared to our manually optimized strain. A transformative result."
Our platform builds on peer-reviewed methods and foundational AI models published in leading scientific journals
Jumper, J., Evans, R., Pritzel, A., et al. Highly accurate protein structure prediction with Enginoma Structure. Nature 596, 583-589 (2021). https://doi.org/10.1038/s41586-021-03819-2
Foundational Enginoma Structure model for accurate protein structure prediction.Lin, Z., Aokabi, H., Baltzis, A., et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123-1130 (2023). https://doi.org/10.1126/science.ade2574
Enginoma sequence models protein language model for evolutionary-scale protein structure and function prediction.Yeh, A.H.-W., Norn, C., Kipnis, Y., et al. De novo design of luciferases using deep learning. Nature 614, 774-780 (2023). https://doi.org/10.1038/s41586-023-05696-3
Deep learning design of custom luciferases from scratch.Notin, P., Dias, M., Frazer, J., et al. Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML 2022 (2022). https://proceedings.mlr.press/v162/notin22a.html
State-of-the-art protein fitness prediction using autoregressive transformers with retrieval mechanism.Zimmerman L, Alon N, Levin I, et al. Context-dependent design of induced-fit enzymes using deep learning generates well-expressed, thermally stable and active enzymes. PNAS 121, e2313809121 (2024). https://doi.org/10.1073/pnas.2313809121
CoSaNN strategy for thermally stable enzyme design using deep learning.AI-driven services in biotechnology use Enginoma deep learning engines, protein language models, and structure prediction tools to accelerate and enhance the design of enzymes, proteins, microbial strains, and bioinformatics analysis. This approach dramatically reduces experimental screening burden and development timelines compared to traditional methods, enabling us to explore vastly larger design spaces and identify optimal candidates more efficiently.
Our AI platform integrates Enginoma Structure/Enginoma Structure/Enginoma Complex for 3D structure prediction, ESM series and custom-trained protein language models for sequence-function mapping, generative models (including hallucination and inpainting approaches) for de novo design, and high-throughput experimental validation to close the AI-prediction loop. We combine industry-standard state-of-the-art models with proprietary performance-calibrated Enginoma models trained on our own proprietary experimental data accumulated across thousands of projects.
Our AI-driven services span four complementary directions: (1) AI-Driven Enzyme Design Services — covering enzyme activity optimization, stability engineering, substrate specificity, and de novo enzyme creation for all six EC classes; (2) AI-Driven Protein Design Services — covering de novo protein creation, protein-protein interaction interface design, antibody humanization and redesign, and structure-guided protein engineering; (3) AI-Driven Strain Engineering Services — covering metabolic pathway design, chassis cell optimization, gene circuit design, and genome-scale engineering; and (4) AI-Driven Bioinformatics Services — covering multi-omics integration, comparative genomics, differential expression analysis, and custom computational pipeline development.
Yes. Our de novo protein design and enzyme design services can create entirely novel functional proteins from scratch, including enzymes catalyzing non-natural reactions that have no natural template sequence. Using generative AI methods like family-wide hallucination, inverse folding, and diffusion-based protein generation, we can design stable scaffolds with precisely engineered active sites for bespoke biocatalytic applications. We have successfully delivered custom enzymes for reactions considered impossible through traditional directed evolution approaches.
Absolutely. One of our core differentiators is the fully integrated AI-design-to-wet-lab-validationclosed loop (closed loop). AI predictions are validated through our high-throughput screening, expression, and characterization platforms, and the resulting data cycles back to continuously improve our AI models — creating a true data flywheel that accelerates subsequent design cycles. This means you receive not just computational predictions, but experimentally validated lead candidates ready for your application, complete with full characterization data and scale-up support as needed.
Whether you need optimized enzymes, novel proteins, high-performance strains, or deep bioinformatics insights, our AI platform and expert team can deliver results faster and more reliably than traditional approaches.
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