Replace harsh chemical processes with enzyme-catalyzed reactions. We use machine learning and directed evolution to discover, optimize, and scale biocatalysts for pharmaceutical intermediates, fine chemicals, and sustainable manufacturing.
Enzymes offer unparalleled selectivity and operate under mild conditions, but finding the right biocatalyst for each transformation is a bottleneck. AI accelerates every step — from enzyme discovery to process optimization.
Biocatalytic processes operate in aqueous media at ambient temperature and pressure, reducing energy consumption, organic solvent use, and hazardous waste compared to traditional chemical synthesis.
Enzymes distinguish between enantiomers with exquisite precision. AI-guided engineering enhances this inherent selectivity, enabling single-enantiomer production of pharmaceutical intermediates without protecting group chemistry.
ML models trained on sequence-function data predict beneficial mutations, reducing the number of experimental variants that need to be screened from thousands to dozens, compressing development timelines significantly.
Biocatalytic routes often require fewer synthetic steps, eliminate the need for expensive chiral resolving agents, and operate under mild conditions — reducing both raw material costs and capital expenditure.
Generative AI and protein language models explore enzyme sequence space beyond what natural evolution has produced, enabling biocatalysis of reactions for which no natural enzyme currently exists.
Enzyme-catalyzed processes are increasingly preferred by regulatory agencies for pharmaceutical manufacturing due to reduced impurity profiles and greener process attributes.
We deliver AI-engineered biocatalysts across pharmaceutical synthesis, fine chemical production, and specialty manufacturing.
Engineering ketoreductases, transaminases, and lipases for enantioselective synthesis of chiral alcohols, amines, and esters used as pharmaceutical building blocks. ML models optimize activity, selectivity, and solvent tolerance simultaneously.
Engineering aldolases, P450 monooxygenases, and lyases for selective C–C bond formation — one of the most challenging transformations in organic synthesis. AI-guided active site redesign enables new-to-nature biocatalytic reactions.
Genome mining combined with ML-based activity prediction identifies novel enzyme candidates from environmental metagenomes and sequence databases. We prioritize candidates based on predicted substrate scope, stability, and expression feasibility.
ML models optimize temperature, pH, co-solvent ratios, substrate loading, and cofactor regeneration systems. Bayesian optimization iteratively identifies optimal conditions with minimal experimental runs.
Our biocatalysis platform combines computational enzyme design with high-throughput experimental screening and process engineering. We handle the full pipeline from concept to pilot-scale production.
Sequence-function models predict the most beneficial mutations, focusing screening effort on high-probability hits rather than exhaustive libraries.
96- and 384-well plate assays with automated liquid handling enable rapid evaluation of engineered variants against target substrates.
Enzyme engineering is coupled with reaction engineering — cofactor regeneration, substrate feeding strategies, and downstream processing are optimized concurrently.
A structured workflow from target analysis through enzyme engineering and process development to delivery.
Define the target reaction, substrate scope, selectivity requirements, and process constraints.
Mine sequence databases and metagenomes for candidate enzymes. Predict activity and expression feasibility using ML.
Apply ML-guided directed evolution to optimize activity, selectivity, stability, and solvent tolerance.
Optimize reaction conditions, cofactor regeneration, and substrate loading using Bayesian optimization.
Pilot-scale production runs with process analytical data and technology transfer documentation.
Our methodologies are grounded in peer-reviewed research in AI-driven enzyme engineering and biocatalysis.
Xie WJ & Warshel A. "Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering." National Science Review. 2023;10(12):nwad331.
PMID 38299119Wittmann BJ, Johnston KE, Wu Z, Arnold FH. "Advances in machine learning for directed evolution." Current Opinion in Structural Biology. 2021;69:11–18.
PMID 33647531Khan MF & Khan MT. "AI-Driven Enzyme Engineering: Emerging Models and Next-Generation Biotechnological Applications." Molecules. 2025;31(1):45.
PMID 41515342Common questions about AI-driven biocatalysis projects.
Yes. Generative AI and active site redesign enable engineering of promiscuous enzymes to catalyze reactions not found in nature. This is particularly valuable for pharmaceutical synthesis where specific bond formations may have no natural enzyme counterpart.
We work across all seven EC classes: oxidoreductases, transferases, hydrolases, lyases, isomerases, ligases, and translocases. Common biocatalysis targets include ketoreductases, transaminases, lipases, P450 monooxygenases, aldolases, and haloalkane dehalogenases.
We design and optimize cofactor regeneration systems as an integral part of the process development workflow. This includes NAD(P)H regeneration via formate dehydrogenase or glucose dehydrogenase, ATP regeneration systems, and PLP-dependent transaminase optimization.
We deliver pilot-scale process data and technology transfer packages suitable for GMP process development. For full GMP manufacturing, we work with partner organizations or support your internal manufacturing team with detailed process documentation.
Timelines depend on target complexity and engineering requirements. Simple optimization projects (improving activity or stability of an existing enzyme) are generally shorter in duration. Full programs from enzyme discovery through pilot-scale validation require a longer engagement, with the exact timeline determined during initial project scoping.
Tell us about your target reaction and process requirements. We'll propose a biocatalysis strategy within 48 hours.
Tell us about your project and we'll get back within 24 hours.
