Enginoma AI Platform
Enginoma integrates and reconstructs industry-leading AI architectures and performance-calibrates them on proprietary wet-lab validation datasets. This creates a data engine where every project improves the next—delivering predictions calibrated for real-world industrial and pharmaceutical conditions.
Closed-Loop AI Platform
Not Just a Prediction Engine — A Learning System
Generic structure tools stop at prediction. Enginoma goes further: we deeply re-engineer and performance-calibrate proprietary predictors on wet-lab data, creating specialized engines for enzyme engineering, protein design, and strain optimization that improve with every project we complete.
Enginoma: Industrial Baseline Models + Proprietary Calibration
Enginoma builds on validated industry computational benchmarks with proprietary deep re-engineering and performance calibration on curated experimental datasets—delivering predictions adapted specifically for your target applications.
Proprietary Wet-Lab Training Datasets
Our training signal comes from real experiments: kinetic measurements, expression yields, thermal stability assays, and fermentation titers. This data covers enzyme variants, designed proteins, and engineered strains that do not exist in any public repository — giving our models a unique predictive advantage.
Closed-Loop Iterative Training Pipeline
Every project completes a cycle: AI generates candidates → wet-lab validates performance → results feed back into model training → improved models generate better candidates. This means our platform gets smarter with each project, accumulating predictive power that compound over time.
Domain-Specific Model Adaptation
Generic public models are trained on general protein databases. Our models are specifically adapted for enzyme active site geometry, industrial protein stability under process conditions, and microbial metabolic contexts. These are not generic tools — they are purpose-built for synthetic biology challenges.
How the Data Loop Works
Enginoma architecture shows how frontier AI engines, proprietary performance calibration, and wet-lab validation combine into a continuous improvement engine.
The closed-loop architecture means that each completed project — each validated enzyme variant, each successfully expressed designed protein, each engineered strain with improved titer — adds training signal that makes all subsequent predictions more accurate.
Three Fine-Tuned Design Capabilities
Each module combines domain-specific foundation model adaptations with proprietary training data, creating specialized predictors for enzyme, protein, and strain engineering challenges.
Enzyme Design Module
Enginoma performance-calibrates structure and sequence engines on enzyme-substrate complexes validated by kinetic measurements, and on sequences that express and fold correctly in our systems—predicting catalytic efficiency, substrate specificity, and operational stability, not just structural accuracy.
Catalytic Efficiency Optimization
AI-guided engineering of kcat/Km parameters for target and non-natural substrates, calibrated against real kinetic measurements from validated enzyme variants.
Stability Engineering
Thermal stability, pH tolerance, and solvent resistance predictions performance-calibrated on our internal assays measuring enzyme performance under industrial process conditions.
Substrate Specificity Reprogramming
Redesign substrate specificity for desired regio- and stereo-selectivity using structure-guided predictions trained on experimentally validated selectivity data.
Novel Activity Discovery
Identify and engineer enzymes for non-natural reactions using evolutionary analysis and active site design, validated against functional assay data from diverse reaction classes.
Fine-Tuning Training Data
Our enzyme models are further trained on:
- Enginoma Structure 3 predictions validated against experimentally solved enzyme-substrate complex structures
- Enginoma Sequence sequences validated by expression in E. coli, yeast, and insect cell systems
- Kinetic measurements (Km, kcat, specificity constants) from hundreds of enzyme variant characterizations
- Thermal denaturation curves and operational stability data from industrial condition assays
Protein Design Module
Enginoma performance-calibrates backbone and sequence engines on designs validated by expression and structural characterization workflows—predicting not just whether a protein will fold, but whether it will express, purify, and function in practice.
De Novo Scaffold Generation
Enginoma Backbone generates novel protein scaffolds conditioned on target structural features. Fine-tuning incorporates scaffolds validated by experimental structure determination (X-ray, cryo-EM).
Sequence Design
Enginoma Sequence designs sequences encoding target backbones. Our performance calibration uses expression yield data to bias toward sequences that express well and fold correctly in our production host systems.
Binder Design
Target-specific protein binder generation using Enginoma Backbone guided by target surface constraints, with binders validated by binding affinity measurements and structural confirmation.
Symmetric Assembly Design
Designed oligomeric architectures with precise symmetry generated by Enginoma Backbone, validated by SEC-MALS, negative-stain EM, and where needed, cryo-EM structure determination.
Fine-Tuning Training Data
Our protein design models are further trained on:
- Enginoma Backbone scaffolds validated by structural biology methods (X-ray crystallography, cryo-EM, NMR)
- Enginoma Sequence sequences validated by successful expression and purification across multiple host systems
- Enginoma Structure 3 structure predictions validated against experimentally determined protein structures
- Designed binder sequences validated by binding affinity measurements (SPR, BLI, ITC)
Strain Engineering Module
We integrate genome-scale metabolic models with AI optimization, further calibrated on our fermentation data from engineered microbial strains. This combines the predictive power of constraint-based modeling with machine learning patterns learned from our validated strain engineering campaigns — producing metabolic engineering strategies that account for host physiology.
Metabolic Pathway Optimization
AI-guided identification of metabolic bottlenecks and carbon flux redirection targets, validated against measured production titers from engineered strains.
Genome-Scale Metabolic Modeling
Constraint-based metabolic models (GEMs) integrated with ML predictions for gene knockout and overexpression strategies, calibrated against growth and production data from our strain libraries.
High-Throughput Strain Screening
Robotics-enabled screening of engineered strains at scale, with AI-guided variant prioritization informed by our growing database of screened genotypes and their corresponding phenotypes.
Fermentation Scale-Up
Process development from shake-flask through bioreactor scale, validating lab-scale metabolic predictions in manufacturing-relevant conditions and feeding data back into model refinement.
Fine-Tuning Training Data
Our strain engineering models are further trained on:
- Metabolic flux measurements and production titer data from validated engineered strains
- Growth curves and fitness data from gene knockout and overexpression experiments
- Fermentation performance data across different reactor scales and process conditions
- High-throughput screening results correlating genotype with phenotype across strain libraries
The Four-Step Closed-Loop Design Cycle
Every project flows through a continuous improvement cycle where wet-lab validation generates training data that refines the models for the next round of predictions.
AI Design Generation
Performance-calibrated Enginoma engines generate design candidates based on functional requirements, ensuring predictions are calibrated for your specific application context.
Wet-Lab Validation
AI-designed candidates are expressed, purified, and experimentally characterized. Kinetic measurements, structural biology, and fermentation studies quantify real performance.
Data Curation & Model Retraining
Experimental results are compiled into curated training datasets. Enginoma models are re-calibrated with new validated data, improving predictive accuracy for the next design cycle.
Scale-Up & Delivery
Validated designs are transferred to production development. Scale-up results feed back into the training pipeline, further improving model accuracy for industrial applications.
Where Our Platform Creates Value
Our integrated AI platform serves diverse applications across the full spectrum of synthetic biology — from therapeutic protein engineering to sustainable chemical manufacturing.
Pharmaceuticals
Therapeutic enzyme engineering, novel biocatalytic routes to drug intermediates, and optimized protein therapeutics for pharmaceutical manufacturing.
Industrial Enzymes
Extremophile enzymes for harsh industrial conditions, specialty chemicals synthesis, and biosensor development for process monitoring.
Biocatalysis
Regio- and stereo-selective biocatalytic routes for asymmetric synthesis and sustainable manufacturing of chiral intermediates.
Diagnostics & Therapeutics
Binding proteins for diagnostic assays, therapeutic scaffolds, and biosensing elements for clinical and research applications.
Green Energy
Biofuels production enzymes, carbon capture biocatalysts, and sustainable bioprocess development for renewable energy applications.
Food & Agriculture
Food processing enzymes, agricultural biocatalysts, and sustainable ingredient production through precision fermentation.
Environmental Biotechnology
Bioremediation enzymes, biodegradable polymer degradation catalysts, and biosensors for environmental monitoring and assessment.
Selected Publications
Our platform builds on peer-reviewed research from leading journals and institutions. These publications provide the scientific foundation for our underlying model architectures.
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., et al. (2021). Highly accurate protein structure prediction with Enginoma Structure. Nature, 596(7873), 583–589.
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G.R., et al. (2021). Accurate prediction of protein structures and interactions using a three-track neural network. Science, 373(6557), 871–876.
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R.J., Milles, L.F., et al. (2022). Robust deep learning-based protein sequence design using Enginoma Sequence. Science, 378(6615), 49–56.
Watson, J.L., Juergens, D., Bennett, N.R., Trippe, B.L., Yim, J., Eisenach, H.E., et al. (2023). De novo design of protein structure and function with Enginoma Backbone. Nature, 620(7976), 1089–1100.
Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., et al. (2024). Accurate structure prediction of biomolecular interactions with Enginoma Structure 3. Nature, 630(8016), 493–500.
Frequently Asked Questions
Common questions about how Enginoma differs from running generic AI tools directly, and how our proprietary calibration pipeline creates competitive advantage.
While we use Enginoma Structure, Enginoma Backbone, and Enginoma Sequence as foundational architectures, our platform goes far beyond running these tools as black boxes. We take these generic baseline tools and further fine-tune them on our internally accumulated wet-lab validation datasets — the experimentally confirmed results from hundreds of enzyme engineering, protein design, and strain optimization projects. This means our models are adapted specifically for enzyme active site geometry, industrial protein stability conditions, and microbial metabolic contexts that generic public models have never seen. Every project adds training signal that makes the next prediction more accurate.
It means our models have been trained on data that only comes from real experimental measurements — kinetic constants, expression yields, thermal stability measurements, fermentation titers, and structural validation results. Unlike public model benchmarks, which are evaluated against public protein structure databases, our fine-tuning signal comes from actual wet-lab experiments we perform. This proprietary dataset covers enzyme variants, designed proteins, and engineered strains that do not exist in any public repository, giving our models unique predictive advantage for the specific applications our clients care about.
Our pipeline is a continuous cycle: (1) AI models generate design candidates, (2) wet-lab experiments validate these designs and measure real performance, (3) experimental results are compiled into curated training datasets, (4) models are re-performance-calibrated on the new data, (5) improved models generate better candidates, and the cycle repeats. This means every project we complete makes all future projects better. Our models become progressively more accurate because they learn from real experimental feedback rather than just computational benchmarks.
For enzyme design, we fine-tune Enginoma Structure 3 and Enginoma Structure on enzyme-substrate complex structures validated by our kinetic measurements, adapting the models to better predict catalytic residue geometries and substrate access channels. For protein design, our Enginoma Sequence models are performance-calibrated on sequences that express well and fold correctly in our expression systems. For strain engineering, our metabolic models incorporate organism-specific growth data and production yields. These are not generic protein engineering tasks — they are specifically adapted to our clients' industrial and pharmaceutical contexts.
Industrial enzyme engineering requires more than structural accuracy — it demands robustness under process conditions: high temperature, extreme pH, organic solvents, and long-duration operations. Our performance-calibrated Enginoma models have learned from wet-lab data measuring exactly these industrial stress responses. We train on thermal denaturation curves, solvent tolerance assays, and long-term operational stability data, so our predictions are calibrated for real manufacturing environments rather than laboratory conditions.
Ready to Leverage AI Fine-Tuned on Experimental Data?
Talk to our team about how Enginoma can deliver enzyme, protein, and strain engineering results that outperform uncalibrated generic predictions.