Automates ligand discovery targeting specific protein pathways using advanced neural models.
Applies across biomaterials, therapeutics, and industrial biomanufacturing with minimal customization.
Dramatically reduces the cost of high-performance ligands without compromising quality.
Cutting reliance on expensive recombinant proteins and reducing per-liter media costs dramatically.
Provides an in-silico media prototyping engine that rapidly screens molecules for biological function, stability, and scalability—reducing time-to-decision and R&D cost by up to 60%.
Integrates regulatory intelligence into media optimization—flagging GRAS-compatible inputs and simulating bioreactor-scale conditions early, enabling smoother tech transfer and faster regulatory pathways.
Designs AI-generated small molecules that mimic growth factors, optimized for receptor binding, species compatibility, and food safety.
Predicts cell growth and differentiation outcomes using AI models trained on wet-lab data, reducing trial-and-error and lab costs.
Uses reinforcement learning to balance ingredient cost, chemical stability, and biological performance for scalable media formulations.
Screens for ingredients with FDA, GRAS, or EFSA precedent to ease regulatory approval and accelerate commercialization.