From target identification and biologic design to enzyme-based synthesis and process optimization — AI methods that accelerate and derisk every stage of pharmaceutical development.
Traditional pharmaceutical development is slow not because the science is hard, but because the search space is enormous. Screening millions of compound–target combinations, testing hundreds of formulation variants, troubleshooting fermentation batches — these take time and resources that most programs cannot afford to waste.
AI changes the economics. Machine learning models trained on structural, genomic, and process data can prioritize candidates, predict failures before experiments, and optimize conditions in silico. The wet lab time you do spend is spent on higher-probability work.
Enginoma Structure predictions and molecular dynamics simulations guide rational engineering of therapeutic proteins and biologic candidates with defined binding geometry.
Enzymatic synthesis pathways reduce reliance on toxic reagents and heavy metal catalysts, lowering environmental burden and simplifying API manufacturing.
Data-driven models predict optimal fermentation conditions, purification parameters, and formulation variables — cutting development cycles without sacrificing quality.
Specific, practical applications across drug discovery, biologic engineering, synthesis, and manufacturing.
AI analysis of genomic, transcriptomic, and proteomic datasets identifies disease-relevant protein targets, predicts druggability, and prioritizes targets based on structural accessibility and biological relevance.
Protein language models and generative AI design therapeutic proteins — antibodies, enzymes, peptides — with optimized binding affinity, selectivity, and stability profiles for pharmaceutical applications.
Engineered enzymes replace chemical synthesis steps in active pharmaceutical ingredient (API) production — improving selectivity, reducing waste, and enabling stereocontrolled synthesis that chemical methods struggle to match.
Machine learning models trained on formulation databases predict excipient compatibility, stability profiles, and release characteristics — accelerating development of solid, liquid, and biologic dosage forms.
DoE and machine learning methods optimize microbial and mammalian cell culture conditions for biologic production — maximizing titer and product quality while minimizing development time.
Computational tools streamline purification process development, predict chromatography behavior, and support CMC documentation by building data-rich process models from experimental datasets.
A collaborative, milestone-based workflow that keeps your program on track from target to process.
Define the biological question, analyze available data, and identify the AI tools best matched to your discovery challenge.
Computational design of protein or enzyme candidates guided by structural models, sequence databases, and application-specific constraints.
Expression, purification, and functional testing of designed candidates in our integrated wet lab facility, with results feeding back into the design cycle.
Process development, optimization, and scale-up support from lab to pilot, with documentation suitable for regulatory filing.
Selected references supporting our platform approaches.
Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics. 2023. PMID: 37514102
Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with Enginoma Structure. Nature. 2021. PMID: 34265844
We work across small molecule API synthesis (enzyme-catalyzed routes), biologic drug development (therapeutic proteins, antibodies, peptides), and manufacturing process development. Project scope ranges from single enzyme engineering tasks to full pipeline support from target to process.
Enzymatic routes offer advantages in stereocontrol, functional group tolerance, and reaction conditions. They typically operate under mild aqueous conditions, avoiding hazardous reagents and heavy metal catalysts. The key limitation has historically been enzyme stability and substrate scope — gaps that AI-driven enzyme engineering is now closing by designing catalysts tailored to specific substrates and process conditions.
We provide process development documentation, characterization data packages, and analytical reports that support regulatory filing. For GMP manufacturing and regulatory submissions, we work with qualified CMOs and CROs that can serve as formal partners. We can help coordinate and review these handoffs.
Yes. We apply machine learning models to formulation screening, predicting excipient compatibility, aggregation risk, and stability under different storage and delivery conditions. This is particularly useful for biologic drugs where formulation significantly affects shelf life and patient outcomes.
Whether you are at the discovery stage or optimizing a manufacturing process, we can help. Tell us where you are and what you need.
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