AI Use Cases

Traditional drug discovery takes 10-15 years and $2.6B per approved drug, with 90% failure rates in clinical trials. Manual screening of compound libraries cann

Predicting protein-protein interactions and conformational dynamics remains challenging, limiting rational drug design for allosteric modulators and protein-bas

Fragmented data management across spreadsheets, paper notebooks, and disconnected systems wastes 20-30% of researcher time on data organization rather than disc

Scale-up from laboratory fermentation to commercial biomanufacturing introduces unpredictable biological behaviors that invalidate lab-scale optimization result

Clinical genomics demands rapid turnaround — critically ill patients cannot wait days for bioinformatics analysis when time-sensitive treatment decisions depend

Physics-based molecular simulations require massive GPU infrastructure and specialized expertise, limiting access to well-funded pharmaceutical companies. Integ

Training biological AI models from scratch requires massive labeled datasets that are expensive and time-consuming to generate experimentally. Transfer learning

Manual pipetting introduces 5-15% variability in liquid handling, undermining experimental reproducibility and requiring larger sample sizes to achieve statisti

Matching patients to clinical trials requires manually reviewing eligibility criteria across thousands of active studies — a time-consuming process that misses