Phase 1: Data-first, AI-native research and development
Why can't data-driven studies be more accessible?
Biology is so complicated that its R&D can largely benefit from data intensive approach. It requires high-throughput screening, deep learning models, AI-aided design, etc.
None of those platforms is easy or cheap to build. Organizing experts in AI and biology is not easy either, both technology and culture wise.
In phase 1, we provide data-driven solutions to streamline the R&D processes for protein engineering. It integrates AI algorithms, ultra-high-throughput screening, large part registry and pre-trained models. So you could focus on proteins themselves.
- Turnaround time: Starting from 6 months.
- Deliverables: Technical reports with raw datasets
Phase 2: Flexible protein production
Why can't protein production be easier?
After finishing the R&D, you may want to produce your proteins to test with the downstream applications. Although recombinant protein production seems simple in principle, it was never easy in practice:
- 50% proteins failed to express (Bhandari et al. 2021)
- 20% proteins failed to purify (Braun et al. 2001)
In addition, scaling up bioproduction is technically difficult. And it is even worse when adapting the production scale to demands.
In phase 2, we provide solutions to produce and purify desired proteins on a flexible scale. So you don't need to worry about building the bioreactors, optimizing the processing, or managing the production scales.
- Turnaround time: Starting from 3 month.
- Deliverables: Protein (>80% purity) at target amount with QC reports