We design and engineer de novo biosynthetic pathways for sustainable production of high-value compounds. Our AI platform combines retrosynthesis, enzyme matching, and pathway optimization to deliver production-ready strains.
Traditional pathway design relies on manual curation of metabolic databases and limited experimental screening. Our AI platform accelerates biosynthetic pathway discovery by computationally exploring thousands of potential routes before experimental validation.
AI-guided retrosynthesis explores thousands of potential biosynthetic routes in hours. Our deep learning models predict novel enzyme combinations and pathway architectures that may not exist in nature.
Machine learning models trained on enzyme promiscuity data predict cross-reactivity and substrate specificity. We match enzymes from diverse sources to build functional pathways with high catalytic efficiency.
Our platform considers host metabolism, precursor availability, and regulatory constraints during pathway design. We optimize pathway-host interactions to maximize product yields and cellular fitness.
Our algorithms predict and mitigate pathway bottlenecks, toxic intermediate accumulation, and metabolic burden. Dynamic regulation systems and compartmentalization strategies ensure stable pathway operation.
From target compound analysis to production strain development, our platform covers the full pathway design workflow.
Our deep learning models trained on reaction databases perform retrosynthetic analysis to identify novel biosynthetic routes. We explore both canonical and non-canonical transformations to design pathways de novo.
We mine enzymes from genomic, metagenomic, and protein family databases. Machine learning models predict enzyme promiscuity and substrate specificity to identify optimal enzyme candidates.
We model host metabolism and identify precursor availability, cofactor balance, and regulatory constraints. Our platform optimizes pathway expression, localization, and dynamic regulation for maximum productivity.
A streamlined process from target specification to validated pathway design.
We analyze your target compound's chemical structure, biosynthesis feasibility, and market applications to define pathway design objectives.
Our AI explores thousands of potential biosynthetic routes, evaluating thermodynamic feasibility, enzyme availability, and pathway complexity.
We identify and rank enzyme candidates from diverse sources, predicting activity, specificity, and compatibility with the host chassis.
We optimize pathway-host integration, including expression constructs, regulatory systems, and fermentation strategies for production.
Our metabolic pathway design platform supports production of diverse compound classes across multiple industries.
Design pathways for APIs, intermediates, and drug precursors. We work with complex natural product scaffolds and novel therapeutic compounds.
Produce high-value specialty chemicals, flavor compounds, fragrances, and agricultural intermediates through sustainable biosynthesis.
Engineer pathways for vitamins, amino acids, antioxidants, and other health-promoting compounds for food and supplement applications.
Our platform builds upon peer-reviewed research in computational metabolic engineering and synthetic biology.
Xie, Y., Chen, Y., & Wang, J. (2024). Deep learning in template-free de novo biosynthetic pathway design. Briefings in Bioinformatics, 25(6), bbae495.
Briefings in Bioinformatics | DOI: 10.1093/bib/bbae495Kumar, A., Wang, L., Ng, C. Y., & Zhao, H. (2018). Pathway design using de novo steps through uncharted biosynthetic spaces. Nature Communications, 9(1), 4908.
Nature Communications | DOI: 10.1038/s41467-017-02362-xWang, L., Dash, S., Ng, C. Y., & Maranas, C. D. (2017). A review of computational tools for design and engineering of biochemical pathways. Synthetic and Systems Biotechnology, 2(4), 243-252.
Synthetic and Systems Biotechnology | DOI: 10.1016/j.synbio.2017.11.002Chen, J., Singh, N., Lu, J., Lane, S. T., & Zhao, H. (2025). Artificial intelligence–powered biofoundries for protein engineering and metabolic engineering. Current Opinion in Biotechnology, 96, 103380.
Current Opinion in Biotechnology | DOI: 10.1016/j.copbio.2025.103380Common questions about our metabolic pathway design services.
We design pathways for a wide range of compounds including pharmaceuticals, nutraceuticals, fine chemicals, biofuels, and agricultural compounds. The Enginoma platform supports both native and non-native metabolite production in microbial hosts.
We use deep learning models trained on enzyme promiscuity and reaction rules to predict novel biosynthetic routes. Our algorithms explore thousands of potential pathways and rank them by predicted feasibility, enzyme availability, and metabolic burden.
Yes. Our platform considers intermediate toxicity, metabolic burden, and host fitness in pathway ranking. We can design compartmentalized pathways, dynamic regulation systems, or use orthogonal pathways to manage toxic intermediates.
Yes. We integrate enzyme mining from genomic and metagenomic databases with machine learning-based activity prediction. Our platform identifies enzyme candidates, predicts activity on non-natural substrates, and optimizes sequences for improved catalytic properties.
We work with E. coli, yeast (S. cerevisiae), Bacillus, Pseudomonas, filamentous fungi, and plant systems. Our platform models host metabolism and considers chassis-specific constraints during pathway design.
Contact our team to discuss your target compound and pathway design requirements. We'll provide a customized proposal for your project.
Tell us about your project and we'll get back within 24 hours.
